Using range and inertia sensors for trajectory and pose estimation

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.