Author name cluster

Alberto Sanfeliu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

37 papers

1 author row

IROS Conference 2025 Conference Paper

Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers

Gerard Gómez-Izquierdo
Javier Laplaza
Alberto Sanfeliu
Anaís Garrell

Accurate human motion prediction (HMP) is critical for seamless human-robot collaboration, particularly in handover tasks that require real-time adaptability. Despite the high accuracy of state-of-the-art models, their computational complexity limits practical deployment in real-world robotic applications. In this work, we enhance human motion forecasting for handover tasks by leveraging siMLPe [1], a lightweight yet powerful architecture, and introducing key improvements. Our approach, named IntentMotion incorporates intention-aware conditioning, task-specific loss functions, and a novel intention classifier, significantly improving motion prediction accuracy while maintaining efficiency. Experimental results demonstrate that our method reduces body loss error by over 50%, achieves 200× faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models in robotics. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction.

ICRA Conference 2024 Conference Paper

Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks

Jose Enrique Domínguez-Vidal
Alberto Sanfeliu

In this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human’s force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our previous predictor by achieving a success rate of 93. 8% in testset and 90. 9% in real experiments with 21 volunteers predicting in both cases the force that the human will exert during the next 1 s. A modification in the architecture allows us to obtain a second output from the model with a velocity prediction, which allows us to improve the capabilities of our predictor if it is used to estimate the trajectory that the human-robot pair will follow. An ablation test is also performed to verify the relative contribution to performance of each input.

IROS Conference 2024 Conference Paper

Force and Velocity Prediction in Human-Robot Collaborative Transportation Tasks through Video Retentive Networks

Jose Enrique Domínguez-Vidal
Alberto Sanfeliu

In this article, we propose a generalization of a Deep Learning State-of-the-Art architecture such as Retentive Networks so that it can accept video sequences as input. With this generalization, we design a force/velocity predictor applied to the medium-distance Human-Robot collaborative object transportation task. We achieve better results than with our previous predictor by reaching success rates in testset of up to 93. 7% in predicting the force to be exerted by the human and up to 96. 5% in the velocity of the human-robot pair during the next 1 s, and up to 91. 0% and 95. 0% respectively in real experiments. This new architecture also manages to improve inference times by up to 32. 8% with different graphics cards. Finally, an ablation test allows us to detect that one of the input variables used so far, such as the position of the task goal, could be discarded allowing this goal to be chosen dynamically by the human instead of being pre-set.

IROS Conference 2024 Conference Paper

Perception-Driven Shared Control Architecture for Agricultural Robots Performing Harvesting Tasks

Jozsef Palmieri
Paolo Di Lillo
Alberto Sanfeliu
Alessandro Marino

This paper introduces a shared control framework designed specifically for agricultural mobile manipulators engaged in harvesting operations. The shared control strategy allows for achieving such operations by dynamically exchanging the control between the robotic system and a human operator depending on the uncertainty in the environment perception. For this purpose, the robot’s behavior is dynamically adapted to switch between two control modes with a different level of autonomy of the robot. The level of autonomy is encoded in two different admittance behaviors which are included in a first-order Hierarchical Quadratic Programming (HQP) control framework, that allows the robot to simultaneously address other control objectives at the same time. Experimental results with a dual-arm mobile robot, developed as part of the EU-funded CANOPIES project, demonstrate the effectiveness of the proposed method in real conditions.

IROS Conference 2023 Conference Paper

Improving Human-Robot Interaction Effectiveness in Human-Robot Collaborative Object Transportation Using Force Prediction

Jose Enrique Domínguez-Vidal
Alberto Sanfeliu

In this work, we analyse the use of a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We check that this force prediction can improve multiple parameters associated with effective Human-Robot Interaction (HRI) such as perception of the robot's contribution to the task, comfort or trust in the robot in a physical Human Robot Interaction (pHRI). We present a Deep Learning model that allows to predict the force that a human will exert in the next 1 $s$ using as inputs the force previously exerted by the human, the robot's velocity and environment information obtained from the robot's LiDAR. Its success rate is up to 92. 3% in testset and up to 89. 1 % in real experiments. We demonstrate that this force prediction, in addition to being able to be used directly to detect changes in the human's intention, can be processed to obtain an estimate of the human's desired trajectory. We have validated this approach with a user study involving 18 volunteers.

IROS Conference 2022 Conference Paper

Context and Intention aware 3D Human Body Motion Prediction using an Attention Deep Learning model in Handover Tasks

Javier Laplaza
Francesc Moreno-Noguer
Alberto Sanfeliu

This work explores how contextual information and human intention affect the motion prediction of humans during a handover operation with a social robot. By classifying human intention in four different classes, we developed a model able to generate a different motion for each intention class. Furthermore, the model uses a multi-headed attention architecture to add contextual information to the pipeline, such as the position of the robot end effector (REE) or the position of obstacles in the interaction scene. We generate predictions up to two and half seconds in the future given an input sequence of one second containing the previous motion of the human. The results show an improvement of the prediction accuracy, both for the full skeleton prediction and the human hand used for the delivery. The model also allows to generate different sequences with the desired human intention.

ICRA Conference 2021 Conference Paper

Human-Robot Collaborative Multi-Agent Path Planning using Monte Carlo Tree Search and Social Reward Sources

Marc Dalmasso
Anaís Garrell
José Enrique Domínguez
Pablo Jiménez
Alberto Sanfeliu

The collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, a multi-agent plan for both itself and the human. Then, both plans are submitted to human scrutiny, who either agrees or modifies it forcing the robot to adapt its own restrictions or preferences. This process is repeated along the search task as many times as required by the human. Our planner is based on a decentralized variant of Monte Carlo Tree Search (MCTS), with one robot and one human as agents. Moreover, our algorithm allows the robot and the human to optimize their own actions by maintaining a probability distribution over the plans in a joint-action space. The method allows an objective function definition over action sequences, it assumes intermittent communication, it is anytime and suitable for on-line replanning. To test it, we have developed a human-robot communication mobile phone interface. Validation is provided by real-life search experiments of a Parcheesi token in an urban space, including also an acceptability study.

IROS Conference 2019 Conference Paper

People's V-Formation and Side-by-Side Model Adapted to Accompany Groups of People by Social Robots

Ely Repiso
Francesco Zanlungo
Takayuki Kanda 0001
Anaís Garrell
Alberto Sanfeliu

This paper presents a new method to allow robots to accompany a person or a group of people imitating pedestrians behavior. Two-people groups usually walk in a side-by-side formation and three-people groups walk in a V-formation so that they can see each other. For this reason, the proposed method combines a Side-by-side and V-formation pedestrian model with the Anticipative Kinodynamic Planner (AKP). Combining these methods, the robot is able to do an anticipatory accompaniment of groups of humans, as well as to avoid static and dynamic obstacles in advance, while keeping the prescribed formations. The proposed framework allows also a dynamical re-positioning of the robot, if the physical position of the partners change in the group formation. Furthermore, people have a randomness factor that the robot has to manage, for that reason, the system was adapted to deal with changes in people’s velocity, orientation and occlusions. Finally, the method has been validated using synthetic experiments and real-life experiments with our Tibi robot. In addition, a user study has been realized to reveal the social acceptability of the method.

IROS Conference 2019 Conference Paper

Teaching a Drone to Accompany a Person from Demonstrations using Non-Linear ASFM

Anaís Garrell
Carles Coll
René Alquézar
Alberto Sanfeliu

In this paper, we present a new method based on the Aerial Social Force Model (ASFM) to allow human-drone side-by-side social navigation in real environments. To tackle this problem, the present work proposes a new nonlinear-based approach using Neural Networks. To learn and test the rightness of the new approach, we built a new dataset with simulated environments and we recorded motion controls provided by a human expert tele-operating the drone. The recorded data is then used to train a neural network which maps interaction forces to acceleration commands. The system is also reinforced with a human path prediction module to improve the drone’s navigation, as well as, a collision detection module to completely avoid possible impacts. Moreover, a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies. The method was validated by a large set of simulations; we also conducted real-life experiments with an autonomous drone to verify the framework described for the navigation process. In addition, a user study has been realized to reveal the social acceptability of the method.

ICRA Conference 2018 Conference Paper

Deep Lidar CNN to Understand the Dynamics of Moving Vehicles

Victor Vaquero
Alberto Sanfeliu
Francesc Moreno-Noguer

Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e. g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the “observer” vehicle from that of the external “observed” vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.

IROS Conference 2018 Conference Paper

Robot Approaching and Engaging People in a Human-Robot Companion Framework

Ely Repiso
Anaís Garrell
Alberto Sanfeliu

This paper presents a new model to make robots capable of approaching and engaging people with a human-like behavior, while they are walking in a side-by-side formation with a person. This method extends our previous work [1], which allows the robot to adapt its navigation behaviour according to the person being accompanied and the dynamic environment. In the current work, the robot is able to predict the best encounter point between the human-robot group and the approached person. Then, in the encounter point the robot modifies its position to achieve an engagement with both people. The encounter point is computed using a gradient descent method that takes into account all people predictions. Moreover, we make use of the Extended Social Force Model (ESFM), and it is modified to include the dynamic goal. The method has been validated over several situations and in real-life experiments, in addition, a user study has been realized to reveal the social acceptability of the robot in this task.

IROS Conference 2017 Conference Paper

Aerial social force model: A new framework to accompany people using autonomous flying robots

Anaís Garrell
Luis Garza-Elizondo
Michael Villamizar
Fernando Herrero
Alberto Sanfeliu

In this paper, we propose a novel Aerial Social Force Model (ASFM) that allows autonomous flying robots to accompany humans in urban environments in a safe and comfortable manner. To date, we are not aware of other state-of-the-art method that accomplish this task. The proposed approach is a 3D version of the Social Force Model (SFM) for the field of aerial robots which includes an interactive human-robot navigation scheme capable of predicting human motions and intentions so as to safely accompany them to their final destination. ASFM also introduces a new metric to fine-tune the parameters of the force model, and to evaluate the performance of the aerial robot companion based on comfort and distance between the robot and humans. The presented approach is extensively validated in diverse simulations and real experiments, and compared against other similar works in the literature. ASFM attains remarkable results and shows that it is a valuable framework for social robotics applications, such as guiding people or human-robot interaction.

IROS Conference 2017 Conference Paper

On-line adaptive side-by-side human robot companion in dynamic urban environments

Ely Repiso
Gonzalo Ferrer 0001
Alberto Sanfeliu

This paper presents an adaptive side-by-side human-robot companion approach for navigation in urban dynamic environments, based on the anticipative kinodynamic planning. The adaptive means that the robot is capable of adjusting its motion to the behavior of the person being accompanied. Our main objective is to optimize in real time the path performed by the pair human-robot, by modifying dynamically the angle and distance between both throughout different locations of the path. We have defined a new cost function for finding the best planned path that takes into account the cost of the geometrical configuration between the human and the robot. Moreover, we have modified the Extended Social Force Model (SFM) to include the required forces to maintain the angle and distance between the robot and human while the human-robot pair is moving towards the shared goal. The method has been validated throughout a large set of simulations and real-live experiments.

ICRA Conference 2017 Conference Paper

PL-SLAM: Real-time monocular visual SLAM with points and lines

Albert Pumarola
Alexander Vakhitov
Antonio Agudo
Alberto Sanfeliu
Francesc Moreno-Noguer

Low textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called “Manhattan worlds”, where structured edges are predominant. In this paper we propose a solution to handle these situations. Specifically, we build upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extend its formulation to simultaneously handle both point and line correspondences. We propose a solution that can even work when most of the points are vanished out from the input images, and, interestingly it can be initialized from solely the detection of line correspondences in three consecutive frames. We thoroughly evaluate our approach and the new initialization strategy on the TUM RGB-D benchmark and demonstrate that the use of lines does not only improve the performance of the original ORB-SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.

IROS Conference 2016 Conference Paper

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

Ignacio Alzugaray
Alberto Sanfeliu

We propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human's pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.

ICRA Conference 2015 Conference Paper

Efficient monocular pose estimation for complex 3D models

Antonio Rubio 0001
Michael Villamizar
Luis Ferraz
Adrián Peñate Sánchez
Arnau Ramisa
Edgar Simo-Serra
Alberto Sanfeliu
Francesc Moreno-Noguer

We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.

ICRA Conference 2015 Conference Paper

Modeling robot's world with minimal effort

Michael Villamizar
Anaís Garrell
Alberto Sanfeliu
Francesc Moreno-Noguer

We propose an efficient Human Robot Interaction approach to efficiently model the appearance of all relevant objects in robot's environment. Given an input video stream recorded while the robot is navigating, the user just needs to annotate a very small number of frames to build specific classifiers for each of the objects of interest. At the core of the method, there are several random ferns classifiers that share the same features and are updated online. The resulting methodology is fast (runs at 8 fps), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in outdoor and challenging scenarios containing a multitude of different objects. We show that the human can, with minimal effort, provide the robot with a detailed model of the objects in the scene.

IROS Conference 2015 Conference Paper

Multi-objective cost-to-go functions on robot navigation in dynamic environments

Gonzalo Ferrer 0001
Alberto Sanfeliu

In our previous work [1] we introduced the Anticipative Kinodynamic Planning (AKP): a robot navigation algorithm in dynamic urban environments that seeks to minimize its disruption to nearby pedestrians. In the present paper, we maintain all the advantages of the AKP, and we overcome the previous limitations by presenting novel contributions to our approach. Firstly, we present a multi-objective cost function to consider different and independent criteria and a well-posed procedure to build a joint cost function in order to select the best path. Then, we improve the construction of the planner tree by introducing a cost-to-go function that will be shown to outperform a classical Euclidean distance approach. In order to achieve real time calculations, we have used a steering heuristic that dramatically speeds up the process. Plenty of simulations and real experiments have been carried out to demonstrate the success of the AKP.

ICRA Conference 2014 Conference Paper

Behavior estimation for a complete framework for human motion prediction in crowded environments

Gonzalo Ferrer 0001
Alberto Sanfeliu

In the present work, we propose and validate a complete probabilistic framework for human motion prediction in urban or social environments. Additionally, we formulate a powerful and useful tool: the human motion behavior estimator. Three different basic behaviors have been detected: Aware, Balanced and Unaware. Our approach is based on the Social Force Model (SFM) and the intentionality prediction BHMIP. The main contribution of the present work is to make use of the behavior estimator for formulating a reliable prediction framework of human trajectories under the influence of dynamic crowds, robots, and in general any moving obstacle. Accordingly, we have demonstrated the great performance of our long-term prediction algorithm, in real scenarios, comparing to other prediction methods.

ICRA Conference 2014 Conference Paper

Fast online learning and detection of natural landmarks for autonomous aerial robots

Michael Villamizar
Alberto Sanfeliu
Francesc Moreno-Noguer

We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target. A Shannon entropy measure is used to pick the most informative locations of these features. This minimizes the number of Ferns while maximizing its discriminative power, allowing thus, for robust detections at low computational costs. In addition, after offline initialization, the new incoming detections are used to update the posterior probabilities on the fly, and adapt to changing appearances that can occur due to the presence of shadows or occluding objects. All these virtues, make the proposed detector appropriate for UAV navigation. Besides the synthetic experiments that will demonstrate the theoretical benefits of our formulation, we will show applications for detecting landing areas in regions with highly repetitive patterns, and specific objects under the presence of cast shadows or sudden camera motions.

ICRA Conference 2014 Conference Paper

On-board real-time pose estimation for UAVs using deformable visual contour registration

Adrian Amor-Martinez
Alberto Ruiz
Francesc Moreno-Noguer
Alberto Sanfeliu

We present a real time algorithm for estimating the pose of non-planar objects on which we have placed a visual marker. It is designed to overcome the limitations of small aerial robots, such as slow CPUs, low image resolution and geometric distortions produced by wide angle lenses or viewpoint changes. The method initially registers the shape of a known marker to the contours extracted in an image. For this purpose, and in contrast to state-of-the art, we do not seek to match textured patches or points of interest. Instead, we optimize a geometric alignment cost computed directly from raw polygonal representations of the observed regions using very simple and efficient clipping algorithms. Further speed is achieved by performing the optimization in the polygon representation space, avoiding the need of 2D image processing operations. Deformation modes are easily included in the optimization scheme, allowing an accurate registration of different markers attached to curved surfaces using a single deformable prototype. Once this initial registration is solved, the object pose is retrieved using a standard PnP approach. As a result, the method achieves accurate object pose estimation in real-time, which is very important for interactive UAV tasks, for example for short distance surveillance or bar assembly. We present experiments where our method yields, at about 30Hz, an average error of less than 5mm in estimating the position of a 19×19mm marker placed at 0. 7m of the camera.

IROS Conference 2014 Conference Paper

Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

Gonzalo Ferrer 0001
Alberto Sanfeliu

This paper presents a novel approach for robot navigation in crowded urban environments where people and objects are moving simultaneously while a robot is navigating. Avoiding moving obstacles at their corresponding precise moment motivates the use of a robotic planner satisfying both dynamic and nonholonomic constraints, also referred as kynodynamic constraints. We present a proactive navigation approach with respect its environment, in the sense that the robot calculates the reaction produced by its actions and provides the minimum impact on nearby pedestrians. As a consequence, the proposed planner integrates seamlessly planning and prediction and calculates a complete motion prediction of the scene for each robot propagation. Making use of the Extended Social Force Model (ESFM) allows an enormous simplification for both the prediction model and the planning system under differential constraints. Simulations and real experiments have been carried out to demonstrate the success of the proactive kinodynamic planner.

IROS Conference 2013 Conference Paper

Robot companion: A social-force based approach with human awareness-navigation in crowded environments

Gonzalo Ferrer 0001
Anaís Garrell
Alberto Sanfeliu

Robots accompanying humans is one of the core capacities every service robot deployed in urban settings should have. We present a novel robot companion approach based on the so-called Social Force Model (SFM). A new model of robot-person interaction is obtained using the SFM which is suited for our robots Tibi and Dabo. Additionally, we propose an interactive scheme for robot's human-awareness navigation using the SFM and prediction information. Moreover, we present a new metric to evaluate the robot companion performance based on vital spaces and comfortableness criteria. Also, a multimodal human feedback is proposed to enhance the behavior of the system. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.

IROS Conference 2010 Conference Paper

Autonomous navigation for urban service mobile robots

Andreu Corominas Murtra
Eduard Trulls
Oscar Sandoval Torres
Joan Pérez-Ibarz
Dizan Vasquez
Josep M. Mirats Tur
Miquel Ferrer
Alberto Sanfeliu

We present to the robotic community a fully autonomous navigation solution for mobile robots operating in urban pedestrian areas. We introduce our robots and the experimental zone, overview the architecture of the navigation framework, and present the results after 3. 5km of autonomous navigation. We expose the main lessons learnt by the scientific team and identify the issues to improve future works.

IROS Conference 2010 Conference Paper

Local optimization of cooperative robot movements for guiding and regrouping people in a guiding mission

Anaís Garrell
Alberto Sanfeliu

This article presents a novel approach for optimizing locally the work of cooperative robots and obtaining the minimum displacement of humans in a guiding people mission. Unlike other methods, we consider situations where individuals can move freely and can escape from the formation, moreover they must be regrouped by multiple mobile robots working cooperatively. The problem is addressed by introducing a “Discrete Time Motion” model (DTM) and a new cost function that minimizes the work required by robots for leading and regrouping people. The guiding mission is carried out in urban areas containing multiple obstacles and building constraints. Furthermore, an analysis of forces actuating among robots and humans is presented throughout simulations of different situations of robot and human configurations and behaviors.

IROS Conference 2010 Conference Paper

Model validation: Robot behavior in people guidance mission using DTM model and estimation of human motion behavior

Anaís Garrell
Alberto Sanfeliu

This paper describes the validation process of a simulation model that have been used to explore the new possibilities of interaction when humans are guided by teams of robots that work cooperatively in urban areas. The set of experiments, which have been recorded as video sequences, show a group of people being guided by a team of three people (who play the role of the guide robots). The model used in the simulation process is called Discrete Time Motion model (DTM) described in [7], where the environment is modeled using a set of potential fields, and people's motion is represented through tension functions. The video sequences were recorded in an urban space of 10: 000 m 2 denominated Barcelona Robot Lab, where people move in the urban space following diverse trajectories. The motion (pose and velocity) of people and robots extracted from the video sequences were compared against the predictions of the DTM model. Finally, we checked the proper functioning of the model by studying the position error differences of the recorded and simulated sequences.

ICRA Conference 2009 Conference Paper

Combining color-based invariant gradient detector with HoG descriptors for robust image detection in scenes under cast shadows

Michael Villamizar
Jorge Scandaliaris
Alberto Sanfeliu
Juan Andrade-Cetto

In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HoG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.

IROS Conference 2009 Conference Paper

Discrete time motion model for guiding people in urban areas using multiple robots

Anaís Garrell
Alberto Sanfeliu
Francesc Moreno-Noguer

We present a new model for people guidance in urban settings using several mobile robots, that overcomes the limitations of existing approaches, which are either tailored to tightly bounded environments, or based on unrealistic human behaviors. Although the robots motion is controlled by means of a standard particle filter formulation, the novelty of our approach resides in how the environment and human and robot motions are modeled. In particular we define a “Discrete-Time- Motion” model, which from one side represents the environment by means of a potential field, that makes it appropriate to deal with open areas, and on the other hand the motion models for people and robots respond to realistic situations, and for instance human behaviors such as “leaving the group” are considered.

IROS Conference 2009 Conference Paper

Integrating asynchronous observations for mobile robot position tracking in cooperative environments

Andreu Corominas Murtra
Josep M. Mirats Tur
Alberto Sanfeliu

This paper presents an asynchronous particle filter algorithm for mobile robot position tracking, taking into account time considerations when integrating observations being delayed or advanced from the prior estiamate time point. The interest of that filter lies in cooperative environments and in fast vehicles. The paper studies the first case, where a sensor network shares perception data with running robots that receive accurate obeservations with large delays due to acquisition, processing and wireless communications. Promising simulated results comparing a basic particle filter and the proposed one are shown. The paper also investigates a situation where a robot is tracking its position, fusing only odometry and observations from a camera network partially covering the robot path.

ICRA Conference 2008 Conference Paper

Efficient active global localization for mobile robots operating in large and cooperative environments

Andreu Corominas Murtra
Josep M. Mirats Tur
Alberto Sanfeliu

This paper presents a novel and efficient framework to the active map-based global localization problem for mobile robots operating in large and cooperative environments. The paper proposes a rational criteria to select the action that minimizes the expected number of remaining position hypotheses, for the single robot case and for the cooperative case, where the lost robot takes advantage of observations coming from a sensor network deployed on the environment or from other localized robots. Efficiency in time complexity is achieved thanks to reasoning in terms of the number of hypotheses instead of in terms of the belief function. Simulation results in a real outdoor environment of 10. 000m 2 are presented validating the presented approach and showing different behaviours for the single robot case and for the cooperative one.

ICRA Conference 2007 Conference Paper

On the Observability of Bearing-only SLAM

Teresa A. Vidal-Calleja
Mitch Bryson
Salah Sukkarieh
Alberto Sanfeliu
Juan Andrade-Cetto

In this paper we present an observability analysis for a mobile robot performing SLAM with a single monocular camera. The aim is to get a better understanding of the well known intuitive behavior of these systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates. The characterisation of the unobservable directions is made using the nullspace basis of the stripped observability matrix. This allows us to identify which vehicle motions are required to maximise the number of observable states in the system, which in turn affects accuracy in the estimation process. The analysis is performed by modelling the system in the continuous time domain as piecewise constant. Simulation results using an extended information filter are shown to verify the results of the observability analysis.

IROS Conference 2007 Conference Paper

Vision-based loop closing for delayed state robot mapping

Viorela Ila
Juan Andrade-Cetto
Rafael Valencia
Alberto Sanfeliu

This paper shows results on outdoor vision-based loop closing for simultaneous localization and mapping. Our experiments show that for loops of over 50 m, the pose estimates maintained with a delayed-state extended information filter are consistent enough to guarantee assertion of vision- based pose constraints for loop closure, provided no necessary information links are added to the estimator. The technique computes relative pose constraints via a robust least squares minimization of 3D point correspondences, which are in turn obtained from the matching of SIFT features over candidate image pairs. We propose a loop closure test that checks both for closeness of means and for highly informative updates at the same time.

ICRA Conference 2006 Conference Paper

Integration of Dependent Bayesian Filters for Robust Tracking

Francesc Moreno-Noguer
Alberto Sanfeliu
Dimitris Samaras

Robotics applications based on computer vision algorithms are highly constrained to indoor environments where conditions may be controlled. The development of robust visual algorithms is necessary for improving the capabilities of many autonomous systems in outdoor and dynamic environments. In particular, this paper proposes a tracking algorithm robust to several artifacts which may be found in real world applications, such as lighting changes, cluttered backgrounds and unexpected target movements. In order to deal with these difficulties the proposed tracking methodology integrates several Bayesian filters. Each filter estimates the state of a particular object feature which is conditionally dependent on another feature estimated by a distinct filter. This dependence provides improved representations of the target, allowing to segment it out from the background of the image. We describe the updating procedure of the Bayesian filters by a 'hypotheses generation and correction' scheme. The main difference with respect to previous approaches is that the dependence between filters is considered during the feature observation, i. e. , into the 'hypotheses correction' stage, instead of considering it when generating the hypotheses. This proves to be much more effective in terms of accuracy and reliability

ICRA Conference 2005 Conference Paper

Unscented Transformation of Vehicle States in SLAM

Juan Andrade-Cetto
Teresa A. Vidal-Calleja
Alberto Sanfeliu

In this article we propose an algorithm to reduce the effects caused by linearization in the typical EKF approach to SLAM. The technique consists in computing the vehicle prior using an Unscented Transformation. The UT allows a better nonlinear mean and variance estimation than the EKF. There is no need however in using the UT for the entire vehicle-map state, given the linearity in the map part of the model. By applying the UT only to the vehicle states we get more accurate covariance estimates. The a posteriori estimation is made using a fully observable EKF step, thus preserving the same computational complexity as the EKF with sequential innovation. Experiments over a standard SLAM data set show the behavior of the algorithm.

IROS Conference 2004 Conference Paper

Conditions for suboptimal filter stability in SLAM

Teresa A. Vidal-Calleja
Juan Andrade-Cetto
Alberto Sanfeliu

In this article, we show marginal stability in SLAM, guaranteeing convergence to a non-zero mean state error estimate bounded by a constant value. Moreover, marginal stability guarantees also convergence of the Riccati equation of the one-step ahead state error covariance to at least one psd steady state solution. In the search for real-time implementations of SLAM, covariance inflation methods produce a suboptimal filter that eventually may lead to the computation of an unbounded state error covariance. We provide tight constraints in the amount of decorrelation possible, to guarantee convergence of the state error covariance, and at the same time, a linear-time implementation of SLAM.

ICRA Conference 2004 Conference Paper

The Effects of Partial Observability in SLAM

Juan Andrade-Cetto
Alberto Sanfeliu

In this article, we show that partial observability hinders full reconstructibility of the state space in SLAM, making the final map estimate dependent on the initial observations, and not guaranteeing convergence to a positive semi-definite covariance matrix. By characterizing the form of the total Fisher information we are able to determine the unobservable state space directions. To overcome this problem, we formulate new fully observable measurement models that make SLAM stable.

ICRA Conference 2003 Conference Paper

Temporal landmark validation in C

Juan Andrade-Cetto
Alberto Sanfeliu

Current techniques to concurrent map building and localization (CML) have been devised for static environments, and lack robustness in more realistic situations. In this communication we provide new ideas that extend the typical stochastic estimation approach to CML, to take into account the dynamics of the environment. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association. The incorporation of a novel temporal landmark quality test, together with the spatial compatibility tests already available, help alleviate the difficulty of data association. We propose a pair of temporal landmark quality functions to aid in those situations in which landmark observations might not be consistent in time; and show how by incorporating these functions, the overall estimation-theoretic approach to CML is improved. Special attention is paid in that the removal of landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature. Namely, asymptotic convergence and full correlation.