A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder

This paper presents a stochastic map building method for mobile robot using a 2-D laser range finder. Unlike other methods that are based on a set of geometric primitives, the presented method builds a map with a set of obstacle regions. In building a map of the environment, the presented algorithm represents the obstacles with a number of stochastic obstacle regions, each of which is characterized by its own stochastic parameters such as mean and covariance. Whereas the geometric primitives based map sometimes does not fit well to sensor data, the presented method reliably represents various types of obstacles including those of irregular walls and sets of tiny objects. Their shapes and features are easily extracted from the stochastic parameters of their obstacle regions, and are used to develop reliable navigation and obstacle avoidance algorithms. The algorithm updates the world map in real time by detecting the changes of each obstacle region. Consequently, it is adequate for modeling the quasi-static environment, which includes occasional changes in positions of the obstacles rather than constant dynamic moves of the obstacles. The presented map building method has successfully been implemented and tested on the ARES-II mobile robot system equipped with a LADAR 2D-laser range finder.     

A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM application.