Author:
Kethavath Raj Kumar
Affiliation:
Intelligent Systems and Robotics Division, Centre for Artificial Intelligence and Robotics and India
Keyword(s):
SLAM, Odometry, LiDAR, IMU, Particle Filter, Mobile Robot.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Real-Time Systems Control
;
Robotics and Automation
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vehicle Control Applications
Abstract:
This paper proposes a novel method for calculation of generalized Odometry using velocities from Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU), discounting velocities from motor encoder values. Further, the estimated velocities are used for the calculation of Odometry using rigid body Newtonian equations. The generalized Odometry and laser scans are used for implementation of the particle filter Simultaneous Localization and Mapping (SLAM) algorithm. This method overcomes errors due to slippages in mobile robots. The outputs of SLAM maps are experimentally validated in both straight and curved trajectories with reference to ground truth maps. SLAM results obtained from the proposed method Odometry is better than the only LiDAR, IMU and Encoder Odometry in an indoor environment for autonomous navigation of mobile robots.