Information based adaptive robotic exploration

TLDR

Exploration that simultaneously maps and localizes in unknown environments relies on accurate localization, as mapping accuracy directly depends on it. The study aims to improve map accuracy during exploration by adaptively choosing control actions that maximize localization precision. Using an occupancy grid and feature‑based SLAM, the system selects actions to maximize Shannon information gain on the map while reducing pose and feature uncertainty. The approach was validated indoors with a laser‑scanned mobile platform, producing accurate maps.

Abstract

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a feature-based Simultaneous Localisation And Mapping (SLAM) algorithm. Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.