Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters

Abstract

A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from single or multiple sensors as well as a robot’s motion. This contribution describes the extension of the Sequential Importance Resampling filter to detect objects based on an analytical sensor model and embedding into Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping scenarios, which outperform the standard Inverse Sensor Model approach.