Localization for a Rescue Robot Based on NDT Scan Matching

Abstract

This paper studied the localization problem for a rescue robot based on laser scan matching and extended Kalman filtering (EKF). Scan matching method based on normal distribution transform (NDT) can avoid hard feature extraction problem by estimation of the probability distribution of laser scan data and localization can be achieved using correlation of the NDT. Based on NDT scan matching, the NDT-EKF algorithm is proposed , which realizes fast and precise localization in rescue environment by fusing odometery data and scan matching together. The NDT-EKF algorithm has been extensively tested and experimental results show its effectiveness and robustness.

Novel Point-to-Point Scan Matching Algorithm Based on Cross-Correlation

“…Conventional scan matching methods use the apparatus of classical mathematics, while probabilistic scan matching methods evaluate the likelihood of a robot being at certain place. Typical examples of conventional and probabilistic methods are the Iterative Closest Point algorithm [6] and the Normal Distribution Transform algorithm [20], respectively.…”