Abstract
Trajectory estimation is important for mobile robots as it can be used in path extraction, distance to target estimation, obstacle avoidance and autonomous control. This work mainly focuses on trajectory and pose estimation based on range and inertia sensors without the need of wheel odometry. Mainly two different approaches are implemented for trajectory and pose estimation namely simultaneous localization and mapping (SLAM) based gMapping and iterative closest point based laser_scan_matcher (LSM) implementation is improved with the use of inertia sensor and kinematic velocity information. These methods are explained in subsections.
Keywords
Odometry
Trajectory
Pose
Computer science
Computer vision
Artificial intelligence
Mobile robot
Inertia
Kinematics
Iterative closest point
Range (aeronautics)
Robot
Simultaneous localization and mapping
Obstacle
Point cloud
Engineering
Geography
Subject Areas
Robotics and Sensor-Based Localization
·Aerospace Engineering ·Physical Sciences
Advanced Vision and Imaging
·Computer Vision and Pattern Recognition ·Physical Sciences
Robotic Path Planning Algorithms
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2019: 1
2025: 1
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