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Full-Text Articles in Physical Sciences and Mathematics
Multi-Sensor Mobile Robot Localization For Diverse Environments, Joydeep Biswas, Manuela M. Veloso
Multi-Sensor Mobile Robot Localization For Diverse Environments, Joydeep Biswas, Manuela M. Veloso
Computer Science Department Faculty Publication Series
Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be “perfect,” or universally applicable to all situations. Laser rangefinders are commonly used for localization, and state-of-theart algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass …
Episodic Non-Markov Localization: Reasoning About Short-Term And Long-Term Features, Joydeep Biswas, Manuela M. Veloso
Episodic Non-Markov Localization: Reasoning About Short-Term And Long-Term Features, Joydeep Biswas, Manuela M. Veloso
Computer Science Department Faculty Publication Series
Markov localization and its variants are widely used for localization of mobile robots. These methods assume Markov independence of observations, implying that observations made by a robot correspond to a static map. However, in real human environments, observations include occlusions due to unmapped objects like chairs and tables, and dynamic objects like humans. We introduce an episodic non-Markov localization algorithm that maintains estimates of the belief over the trajectory of the robot while explicitly reasoning about observations and their correlations arising from unmapped static objects, moving objects, as well as objects from the static map. Observations are classified as arising …
Localization And Navigation Of The Cobots Over Long-Term Deployments, Joydeep Biswas, Manuela M. Veloso
Localization And Navigation Of The Cobots Over Long-Term Deployments, Joydeep Biswas, Manuela M. Veloso
Computer Science Department Faculty Publication Series
For the last three years, we have developed and researched multiple collaborative robots, CoBots, which have been autonomously traversing our multi-floor buildings. We pursue the goal of long-term autonomy for indoor service mobile robots as the ability for them to be deployed indefinitely while they perform tasks in an evolving environment. The CoBots include several levels of autonomy, and in this paper we focus on their localization and navigation algorithms. We present the Corrective Gradient Refinement (CGR) algorithm, which refines the proposal distribution of the particle filter used for localization with sensor observations using analytically computed state space derivatives on …
Depth Camera Based Indoor Mobile Robot Localization And Navigation, Joydeep Biswas, Manuela M. Veloso
Depth Camera Based Indoor Mobile Robot Localization And Navigation, Joydeep Biswas, Manuela M. Veloso
Computer Science Department Faculty Publication Series
The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the …
Corrective Gradient Refinement For Mobile Robot Localization, Joydeep Biswas, Manuela M. Veloso, Brian Coltin
Corrective Gradient Refinement For Mobile Robot Localization, Joydeep Biswas, Manuela M. Veloso, Brian Coltin
Computer Science Department Faculty Publication Series
Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then …
Wifi Localization And Navigation For Autonomous Indoor Mobile Robots, Joydeep Biswas, Manuela M. Veloso
Wifi Localization And Navigation For Autonomous Indoor Mobile Robots, Joydeep Biswas, Manuela M. Veloso
Computer Science Department Faculty Publication Series
Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We …