Jürgen Sturm

ICRA Conference 2017 Conference Paper

De-noising, stabilizing and completing 3D reconstructions on-the-go using plane priors

• Maksym Dzitsiuk
• Jürgen Sturm
• Robert Maier 0001
• Lingni Ma
• Daniel Cremers

Creating 3D maps on robots and other mobile devices has become a reality in recent years. Online 3D reconstruction enables many exciting applications in robotics and AR/VR gaming. However, the reconstructions are noisy and generally incomplete. Moreover, during online reconstruction, the surface changes with every newly integrated depth image which poses a significant challenge for physics engines and path planning algorithms. This paper presents a novel, fast and robust method for obtaining and using information about planar surfaces, such as walls, floors, and ceilings as a stage in 3D reconstruction based on Signed Distance Fields (SDFs). Our algorithm recovers clean and accurate surfaces, reduces the movement of individual mesh vertices caused by noise during online reconstruction and fills in the occluded and unobserved regions. We implemented and evaluated two different strategies to generate plane candidates and two strategies for merging them. Our implementation is optimized to run in real-time on mobile devices such as the Tango tablet. In an extensive set of experiments, we validated that our approach works well in a large number of natural environments despite the presence of significant amount of occlusion, clutter and noise, which occur frequently. We further show that plane fitting enables in many cases a meaningful semantic segmentation of real-world scenes.

ICRA Conference 2017 Conference Paper

TSDF-based change detection for consistent long-term dense reconstruction and dynamic object discovery

• Marius Fehr
• Fadri Furrer
• Ivan Dryanovski
• Jürgen Sturm
• Igor Gilitschenski
• Roland Siegwart
• Cesar Cadena 0001

Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that enables to continuously refine the static map while simultaneously obtaining 3D reconstructions of dynamic objects in the scene. This is a challenging problem because map updates happen incrementally and are often incomplete. Previous work typically performs change detection on point clouds, surfels or maps, which are not able to distinguish between unexplored and empty space. In contrast, our TSDF-based representation naturally contains this information and thus allows us to more robustly solve the scene differencing problem. We demonstrate the algorithms performance as part of a system for unsupervised object discovery and class recognition. We evaluated our algorithm on challenging datasets that we recorded over several days with RGB-D enabled tablets. To stimulate further research in this area, all of our datasets are publicly available 3.

IROS Conference 2015 Conference Paper

2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners

• Joscha-David Fossel
• Karl Tuyls
• Jürgen Sturm

We introduce a novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner. In particular, we propose a fast scan registration algorithm that operates on 2D maps represented as a signed distance function (SDF). Using SDFs as a map representation has several advantages over existing approaches: while classical 2D scan matchers employ brute-force matching to track the position of the robot, signed distance functions are differentiable on large parts of the map. Consequently, efficient minimization techniques such as Gauss-Newton can be applied to find the minimum. In contrast to occupancy grid maps, the environment can be captured with sub-grid cell size precision, which leads to a higher localization accuracy. Furthermore, SDF maps can be triangulated to polygon maps for efficient storage and transfer. In a series of experiments, conducted both in simulation and on a real physical platform, we demonstrate that SDF tracking is more accurate and efficient than previous approaches. We outperform scan matching on occupancy maps in simulation by ~270% in terms of root mean squared deviation (RMSD) with a ~63% lower standard deviation. In the real robot experiments, we obtain a performance advantage of ~14% RMSD with a ~25% lower standard deviation.

ICRA Conference 2014 Conference Paper

Volumetric 3D mapping in real-time on a CPU

• Frank Steinbrücker
• Jürgen Sturm
• Daniel Cremers

In this paper we propose a novel volumetric multi-resolution mapping system for RGB-D images that runs on a standard CPU in real-time. Our approach generates a textured triangle mesh from a signed distance function that it continuously updates as new RGB-D images arrive. We propose to use an octree as the primary data structure which allows us to represent the scene at multiple scales. Furthermore, it allows us to grow the reconstruction volume dynamically. As most space is either free or unknown, we allocate and update only those voxels that are located in a narrow band around the observed surface. In contrast to a regular grid, this approach saves enormous amounts of memory and computation time. The major challenge is to generate and maintain a consistent triangle mesh, as neighboring cells in the octree are more difficult to find and may have different resolutions. To remedy this, we present in this paper a novel algorithm that keeps track of these dependencies, and efficiently updates corresponding parts of the triangle mesh. In our experiments, we demonstrate the real-time capability on a large set of RGB-D sequences. As our approach does not require a GPU, it is well suited for applications on mobile or flying robots with limited computational resources.

IROS Conference 2013 Conference Paper

Dense visual SLAM for RGB-D cameras

• Christian Kerl
• Jürgen Sturm
• Daniel Cremers

In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several state-of-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source.

IROS Conference 2013 Conference Paper

FollowMe: Person following and gesture recognition with a quadrocopter

• Tayyab Naseer
• Jürgen Sturm
• Daniel Cremers

In this paper, we present an approach that allows a quadrocopter to follow a person and to recognize simple gestures using an onboard depth camera. This enables novel applications such as hands-free filming and picture taking. The problem of tracking a person with an onboard camera however is highly challenging due to the self-motion of the platform. To overcome this problem, we stabilize the depth image by warping it to a virtual-static camera, using the estimated pose of the quadrocopter obtained from vision and inertial sensors using an Extended Kalman filter. We show that such a stabilized depth video is well suited to use with existing person trackers such as the OpenNI tracker. Using this approach, the quadrocopter not only obtains the position and orientation of the tracked person, but also the full body pose — which can then for example be used to recognize hand gestures to control the quadrocopter's behaviour. We implemented a small set of example commands (“follow me”, “take picture”, “land”), and generate corresponding motion commands. We demonstrate the practical performance of our approach in an extensive set of experiments with a quadrocopter. Although our current system is limited to indoor environments and small motions due to the restrictions of the used depth sensor, it indicates that there is large potential for such applications in the near future.

IJCAI Conference 2013 Conference Paper

Learning Probabilistic Models for Mobile Manipulation Robots

• Jürgen Sturm
• Wolfram Burgard

Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. In this paper, we present novel approaches to allow mobile maniplation systems to autonomously adapt to new or changing situations. The approaches developed in this paper cover the following four topics: (1) learning the robot’s kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment visual perception, and (4) learning novel manipulation tasks from human demonstrations.

ICRA Conference 2013 Conference Paper

Robust odometry estimation for RGB-D cameras

• Christian Kerl
• Jürgen Sturm
• Daniel Cremers

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

IROS Conference 2012 Conference Paper

A benchmark for the evaluation of RGB-D SLAM systems

• Jürgen Sturm
• Nikolas Engelhard
• Felix Endres
• Wolfram Burgard
• Daniel Cremers

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.

ICRA Conference 2012 Conference Paper

A generalized framework for opening doors and drawers in kitchen environments

• Thomas Rühr
• Jürgen Sturm
• Dejan Pangercic
• Michael Beetz
• Daniel Cremers

In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i. e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51. 9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.

ICRA Conference 2012 Conference Paper

An evaluation of the RGB-D SLAM system

• Felix Endres
• Jürgen Hess 0001
• Nikolas Engelhard
• Jürgen Sturm
• Daniel Cremers
• Wolfram Burgard

We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and generates a dense 3D model of the environment. We present the key features of our approach and evaluate its performance thoroughly on a recently published dataset, including a large set of sequences of different scenes with varying camera speeds and illumination conditions. In particular, we evaluate the accuracy, robustness, and processing time for three different feature descriptors (SIFT, SURF, and ORB). The experiments demonstrate that our system can robustly deal with difficult data in common indoor scenarios while being fast enough for online operation. Our system is fully available as open-source.

IROS Conference 2012 Conference Paper

Camera-based navigation of a low-cost quadrocopter

• Jakob J. Engel
• Jürgen Sturm
• Daniel Cremers

In this paper, we describe a system that enables a low-cost quadrocopter coupled with a ground-based laptop to navigate autonomously in previously unknown and GPS-denied environments. Our system consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion and state estimation and a PID controller to generate steering commands. Next to a working system, the main contribution of this paper is a novel, closed-form solution to estimate the absolute scale of the generated visual map from inertial and altitude measurements. In an extensive set of experiments, we demonstrate that our system is able to navigate in previously unknown environments at absolute scale without requiring artificial markers or external sensors. Furthermore, we show (1) its robustness to temporary loss of visual tracking and significant delays in the communication process, (2) the elimination of odometry drift as a result of the visual SLAM system and (3) accurate, scale-aware pose estimation and navigation.

IROS Conference 2012 Conference Paper

Real-time human motion tracking using multiple depth cameras

• Licong Zhang
• Jürgen Sturm
• Daniel Cremers
• Dongheui Lee

In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example caused by occlusions. By fusing the depth images of all available cameras into one joint point cloud, we can seamlessly incorporate the available information from multiple sensors into the pose estimation. To track the high-dimensional human pose, we employ state-of-the-art annealed particle filtering and partition sampling. We compute the particle likelihood based on the truncated signed distance of each observed point to a parameterized human shape model. We apply a coarse-to-fine scheme to recognize a wide range of poses to initialize the tracker. In our experiments, we demonstrate that our approach can accurately track human motion in real-time (15Hz) on a GPGPU. In direct comparison to two existing trackers (OpenNI, Microsoft Kinect SDK), we found that our approach is significantly more robust for unconstrained motions and under (partial) occlusions.

IROS Conference 2010 Conference Paper

Operating articulated objects based on experience

• Jürgen Sturm
• Advait Jain
• Cyrill Stachniss
• Charles C. Kemp
• Wolfram Burgard

Many tasks that would be of benefit to users in domestic environments require that robots manipulate articulated objects such as doors and drawers. In this paper, we present a novel approach that simultaneously estimates the kinematic model of an articulated object based on the trajectory described by the robot's end effector, and uses this model to predict the future trajectory of the end effector. One advantage of our approach is that the robot can directly use these predictions to generate an equilibrium point control path for operating the mechanism. Additionally, our approach can improve these predictions based on previously learned articulation models. We have implemented and tested our approach on a real mobile manipulator. Through 40 trials, we show that the robot can reliably open various household objects, including cabinet doors, sliding doors, office drawers, and a dishwasher. Furthermore, we demonstrate that using the information from previous interactions as a prior significantly improves the prediction accuracy.

ICRA Conference 2010 Conference Paper

Tactile object class and internal state recognition for mobile manipulation

• Sachin Chitta
• Matthew Piccoli
• Jürgen Sturm

Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. In this work, we present a tactile perception strategy that allows any mobile robot with tactile sensors in its gripper to measure a set of generic tactile features while grasping an object. We propose a hybrid velocity-force controller, that grasps an object safely and reveals at the same time its deformation properties. As an application, we show that a robot can use these features to distinguish the open/closed and fill state of bottles and cans - purely from tactile sensing - from a small training set. To prove that this is a hard recognition problem, we also conducted a comperative study with 17 human test subjects. We found that the recognition rate of the human subjects were comparable to our robotic gripper.

ICRA Conference 2010 Conference Paper

Vision-based detection for learning articulation models of cabinet doors and drawers in household environments

• Jürgen Sturm
• Kurt Konolige
• Cyrill Stachniss
• Wolfram Burgard

Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.

IJCAI Conference 2009 Conference Paper

• Jürgen Sturm
• Vijay Pradeep
• Cyrill Stachniss
• Christian Plagemann
• Kurt Konolige
• Wolfram Burgard

Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.

ICRA Conference 2009 Conference Paper

Imitation learning with generalized task descriptions

• Clemens Eppner
• Jürgen Sturm
• Maren Bennewitz
• Cyrill Stachniss
• Wolfram Burgard

In this paper, we present an approach that allows a robot to observe, generalize, and reproduce tasks observed from multiple demonstrations. Motion capture data is recorded in which a human instructor manipulates a set of objects. In our approach, we learn relations between body parts of the demonstrator and objects in the scene. These relations result in a generalized task description. The problem of learning and reproducing human actions is formulated using a dynamic Bayesian network (DBN). The posteriors corresponding to the nodes of the DBN are estimated by observing objects in the scene and body parts of the demonstrator. To reproduce a task, we seek for the maximum-likelihood action sequence according to the DBN. We additionally show how further constraints can be incorporated online, for example, to robustly deal with unforeseen obstacles. Experiments carried out with a real 6-DoF robotic manipulator as well as in simulation show that our approach enables a robot to reproduce a task carried out by a human demonstrator. Our approach yields a high degree of generalization illustrated by performing a pick-and-place and a whiteboard cleaning task.

IROS Conference 2009 Conference Paper

Object identification with tactile sensors using bag-of-features

• Alexander Schneider
• Jürgen Sturm
• Cyrill Stachniss
• Marco Reisert
• Hans Burkhardt
• Wolfram Burgard

In this paper, we present a novel approach for identifying objects using touch sensors installed in the finger tips of a manipulation robot. Our approach operates on low-resolution intensity images that are obtained when the robot grasps an object. We apply a bag-of-words approach for object identification. By means of unsupervised clustering on training data, our approach learns a vocabulary from tactile observations which is used to generate a histogram codebook. The histogram codebook models distributions over the vocabulary and is the core identification mechanism. As the objects are larger than the sensor, the robot typically needs multiple grasp actions at different positions to uniquely identify an object. To reduce the number of required grasp actions, we apply a decision-theoretic framework that minimizes the entropy of the probabilistic belief about the type of the object. In our experiments carried out with various industrial and household objects, we demonstrate that our approach is able to discriminate between a large set of objects. We furthermore show that using our approach, a robot is able to distinguish visually similar objects that have different elasticity properties by using only the information from the touch sensor.

IROS Conference 2009 Conference Paper

Regression-based online situation recognition for vehicular traffic scenarios

• Daniel Meyer-Delius
• Jürgen Sturm
• Wolfram Burgard

In this paper, we present an approach for learning generalized models for traffic situations. We formulate the problem using a dynamic Bayesian network (DBN) from which we learn the characteristic dynamics of a situation from labeled trajectories using kernel regression. For a new and unlabeled trajectory, we can then infer the corresponding situation by evaluating the data likelihood for the individual situation models. In experiments carried out on laser range data gathered on a car in real traffic and in simulation, we show that we can robustly recognize different traffic situations even from trajectories corresponding to partial situation instances.

ICRA Conference 2008 Conference Paper

Unsupervised body scheme learning through self-perception

• Jürgen Sturm
• Christian Plagemann
• Wolfram Burgard

In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera. Our approach yields a compact Bayesian network for the robot’s kinematic structure including the forward and inverse models relating action signals and body pose. We propose to simultaneously learn local action models for all pairs of perceivable body parts from data generated through random “motor babbling. ” From this repertoire of local models, we construct a Bayesian network for the full system using the pose prediction accuracy on a separate cross validation data set as the criterion for model selection. The resulting model can be used to predict the body pose when no perception is available and allows for gradient-based posture control. In experiments with real and simulated manipulator arms, we show that our system is able to quickly learn compact and accurate models and to robustly deal with noisy observations.