Autonomous Exploration with Exact Inverse Sensor Models

this paper is focused on probabilistic occupancy grid mapping and motion planning such that a robot may build a map and explore a target area autonomously in real time. the desired path of the robot is developed in an optimal fashion to maximize the information gain from the sensor measurements on its path, thereby increasing the accuracy and efficiency of mapping, while explicitly considering the sensor limitations such as the maximum sensing range and viewing angle. most current exploration techniques require frequent human intervention, often developed for omnidirectional sensors with infinite range. the proposed research is based on realistic assumptions on sensor capabilities. the unique contribution is that the mapping and autonomous exploration techniques are systematically developed in a rigorous, probabilistic formulation. the mapping approach exploits the probabilistic properties of the sensor and map explicitly, and the autonomous exploration is designed to maximize the expected map information gain, thereby improving the efficiency of the mapping procedure and the quality of the map substantially. the efficacy of the proposed optimal approach is illustrated by both numerical simulations and experimental results.