Table of Contents
For autonomous robotic systems, Simultaneous Lo- calization and Mapping (SLAM) is often a necessity. In the last few years, several SLAM techniques have been successfully proposed for varying environments. Hence, it is important to measure the performance of these techniques depending on the use case. We present a relative examination and benchmarking of mobile robot trajectories estimated by four ROS-based algorithms in an indoor environment: (a)lidar-based: Hector-SLAM and GMapping and (b)camera-based: RTAB-Map and ORB-SLAM2. A mobile robot was designed with sensors such as 2D lidar, encoders, inertial measurement unit, and depth camera. Thereafter, experiments were performed in an indoor environment, and information containing time-synchronized poses was gathered from sensors by teleoperating the robot on a testing track. Different SLAM systems were tested on this data and were compared with suitable metrics. The results demonstrate encouraging outcomes for lidar-based Hector-SLAM and camera- based ORB-SLAM2 algorithms.
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Plain text:
C. Nehate, R. Shinde, S. Naik, M. Aradwad, A. Bhurke and F. Kazi, "Implementation and Evaluation of SLAM Systems for a Mobile Robot," 2021 IEEE 18th India Council International Conference (INDICON), 2021, pp. 1-6, doi: 10.1109/INDICON52576.2021.9691761.
Bibtex:
@INPROCEEDINGS{9691761,
author={Nehate, Chinmay and Shinde, Rutwik and Naik, Saket and Aradwad, Manish and Bhurke, Anish and Kazi, Faruk},
booktitle={2021 IEEE 18th India Council International Conference (INDICON)},
title={Implementation and Evaluation of SLAM Systems for a Mobile Robot},
year={2021},
volume={},
number={},
pages={1-6},
doi={10.1109/INDICON52576.2021.9691761}}