Paper Info
Title
Depth Camera Based Indoor Mobile Robot Localization And Navigation
Abstract
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 plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.
Year
DOI
Venue
2012
10.1109/icra.2012.6224766
2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
Keywords
Field
DocType
point cloud,path planning,real time,edge detection,lasers,vectors,mobile robots,navigation,obstacle avoidance
Obstacle avoidance,Motion planning,Computer vision,Edge detection,Outlier,Robustness (computer science),Frame rate,Artificial intelligence,Engineering,Point cloud,Mobile robot
Conference
Volume
Issue
ISSN
2012
1
1050-4729
Citations 
PageRank 
References 
87
2.94
14
Authors
2
Name
Order
Citations
PageRank
Joydeep Biswas138030.46
Manuela Veloso28563882.50