Author name cluster

Scott Lenser

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.

8 papers

1 author row

IROS Conference 2023 Conference Paper

Trajectory-Based SLAM for Indoor Mobile Robots with Limited Sensing Capabilities

Yao Chen
Jeremias Rodriguez
Arman Karimian
Benjamin Pheil
Jose Franco
Renaud Moser
Read Sandström
Scott Lenser

In this paper we introduce a novel SLAM system for 2-D indoor environments that relies only on limited sensing. Our fully autonomous system uses only the trajectory of the robot around walls and objects in the environment as landmarks and is capable of robust and long-term exploration and mapping of a broad range of household floor plans. Rank-deficient and full-rank factors are created when the robot observes existing trajectory-based landmarks, and they are filtered and added in a pose graph, which is optimized periodically. The mission space is mapped by efficient adaptive local mapping algorithms. The proposed SLAM system has been extensively tested in various scenarios, and experimental results show its robustness and accuracy.

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ICRA Conference 2018 Conference Paper

Fast Nonlinear Approximation of Pose Graph Node Marginalization

Duy-Nguyen Ta
Nandan Banerjee
Stephen Eick
Scott Lenser
Mario E. Munich

We present a fast nonlinear approximation method for marginalizing out nodes on pose graphs for longterm simultaneous localization, mapping, and navigation. Our approximation preserves the pose graph structure to leverage the rich literature of pose graphs and optimization schemes. By re-parameterizing from absolute-to relative-pose spaces, our method does not suffer from the choice of linearization points as in previous works. We then join our approximation process with a scaled version of the recently-demoted pose-composition approach. Our approach eschews the expenses of many state-of-the-art convex optimization schemes through our efficient and simple O(N 2 ) implementation for a given known topology of the approximate subgraph. We demonstrate its speed and near optimality in practice by comparing against state-of-the-art techniques on popular datasets.

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ICRA Conference 2005 Conference Paper

Non-Parametric Time Series Classification

Scott Lenser
Manuela Veloso

We present an improved state-based prediction algorithm for time series. Given time series produced by a process composed of different underlying states, the algorithm predicts future time series values based on past time series values for each state. Unlike many algorithms, this algorithm predicts a multi-modal distribution over future values. This prediction forms the basis for labelling part of a time series with the underlying state that created it given some labelled example signals. The algorithm is robust to a wide variety of possible types of changes in signals including changes in mean, amplitude, amount of noise, and period. We show results demonstrating that the algorithm successfully segments signals from several robotic sensors generated while performing a variety of simple tasks.

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IROS Conference 2004 Conference Paper

Classification of robotic sensor streams using non-parametric statistics

Scott Lenser
Manuela Veloso

We extend our previous work on a classification algorithm for time series. Given time series produced by different underlying generating processes, the algorithm predicts future time series values based on past time series values for each generator. Unlike many algorithms, this algorithm predicts a distribution over future values. This prediction forms the basis for labelling part of a time series with the underlying generator that created it given some labelled exam piles. The algorithm is robust to a wide variety of possible types of changes in signals including mean shifts, amplitude changes, noise changes, period changes, and changes in signal shape. We improve upon the speed of our previous approach and show the utility of the algorithm for discriminating between different states of the robot/environment from robotic sensor signals.

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ICRA Conference 2003 Conference Paper

Automatic detection and response to environmental change

Scott Lenser
Manuela Veloso

Robots typically have many sensors, which are underutilized. This is usually because no simple mathematical models of the sensors have been developed or the sensors are too noisy to use techniques, which require simple noise models. We propose to use these underutilized sensors to determine the state of the environment in which the robot is operating. Being able to identify the state of the environment allows the robot to adapt to current operating conditions and the actions of other agents. Adapting to current operating conditions makes robot robust to changes in the environment by constantly adapting to the current conditions. This is useful for adapting to different lighting conditions or different flooring conditions amongst many other possible desirable adaptations. The strategy we propose for utilizing these sensors is to group sensor readings into statistical probability distributions and then compare the probability distributions to detect repeated states of the environment.

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IROS Conference 2003 Conference Paper

Visual sonar: fast obstacle avoidance using monocular vision

Scott Lenser
Manuela Veloso

We contribute a fast system for avoiding unknown obstacles on a mobile robot using a simple camera as the only sensor. The vision module detects objects, both known and unknown, around the robot. Unknown objects are detected by paying attention to occlusions of a floor of known colors. Range and angle to the objects is calculated and used to create a radial model of the vicinity of the robot. This modeling component keeps tracks of objects that are currently outside the field of view of the camera allowing the robot to avoid obstacles it is not currently looking at. We show the effectiveness of the vision and modeling algorithms by creating a simple behavior which wanders around while avoiding obstacles.

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ICRA Conference 2000 Conference Paper

Sensor Resetting Localization for Poorly Modelled Mobile Robots

Scott Lenser
Manuela Veloso

We present a new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization. The algorithm adds sensor based re-sampling to Monte Carlo localization when the robot is lost. Sensor resetting localization (SRL) is robust to modelling errors including unmodelled movements and systematic errors. It can be used in real time on systems with limited computational power. The algorithm has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup'99. We present results from the real robots demonstrating the success of the algorithm and results from simulation comparing SRL to Monte Carlo localization.

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ICAPS Conference 2000 Conference Paper

Vision-Servoed Localization and Behavior-Based Planning for an Autonomous Quadruped Legged Robot

Manuela Veloso
Elly Winner
Scott Lenser
James Bruce
Tucker R. Balch

Planning actions for real robots in dynamic, and mlcertain environments is a challenging problem. It is not viable to use a complete model of the world: it is most appropriate to achieve goals mid handle uncertainty by integrating deliberation and behavior-based reactive planning. Wesuccessfully developed a system integrating perception and action for the RoboCup99 Sony legged robot league. The quadruped legged robots are fully autonomous and thus must. have onboard vision, localization a~td action selection. We briefly present our perception algorithm that automatically classifies arid tracks colored blobs in real time. We then briefly introduce our Sensor Resetting I, ocalization (SRL) algorithm which is an extension of Monte Carlo Localization. Vision and localization provide the state input for action selection. Our robust and sensible behavior scheme handles dynamic changes in information accuracy. We developed a utility-based system for using mid acquiring location information. Finally, we have devised several special built-in plans to deal with times when urgent action is needed and the robot cannot afford to colh: ct accurate location information. Wepresent results using the real robots, which demonst. rate the success of our approach. Our team of Sony quadruped legged robots, CMTrio-99, won all but one of its games in RoboCup99, and was awarded third place in the competition.

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