Heuristic Monte Carlo Algorithm for Unmanned Ground Vehicles Realtime Localization and Mapping
Dunjin Chen, Jian Weng, Feiran Huang, Jian L. Zhou, Yijun Mao, Xiaodong Liu
Abstract
Realtime localization and mapping in a cluttered and noisy indoor environment is a major problem in autonomous unmanned ground vehicle (UGV) navigation. this article proposes a Heuristic Monte Carlo algorithm (HMCA) based on the Monte Carlo localization and Discrete Hough Transform (DHT) to build an autonomous navigation system. Specifically, a generalized map-processing method is first presented for the Hough Transform Algorithm (HTA), which can extract and cluster important map feature information and preserve the low computational complexity of real-time processing. Then a set of relative rotation angles and corresponding spatial displacements of the robot are obtained by constructing hough spatial energy spectrum correlation functions as global and local 2D occupancy grid maps (OGMs). Finally, the result is used as the guiding particle set of the Monte Carlo location algorithm. In a simulated scene and a real scene, the algorithm is tested several times, the adequacy of the algorithm is verified, and a map with good self-positioning performance is constructed.