This module estimates in real time the egomotion of a vehicle based solely on laser range data.
This technique calculates the discrepancy between closely spaced 2D laser scans due to the vehicle motion using scan matching techniques.
The result of the scan alignment is converted into a nonlinear motion measurement and fed into a nonholo- nomic extended Kalman filter model.This model better approximates the real motion of the vehicle, when compared to more simplistic models, thus improving performance and im- munity to outliers.
The motion estimate is intended to be used for egomotion compensation in a target tracking algorithm for situation awareness applications; without the knowledge of the vehicle own motion it is impossible to correctly assess the motion of other agents.
The proposed approach is especially attractive as a complement to traditional systems in situations where other more common egomotion sensors, like GPS or wheel encoders, either fail or provide insufficient data. Several recent scan matching algorithms were evaluated for their accuracy and computational speed. The proposed approach performs in real time and provides an accurate estimate of the current robot motion.
The algorithm is validated through experimental test in real world conditions.