Uncertainty-Aware Autonomous Robot Exploration Using Confidence-Rich Localization and Mapping

Information-based autonomous robot exploration methods, aiming to maximize the exploration rewards, e.g., mutual information (MI), get more prevalent in field robotics applications. However, most MI-based exploration methods assume known poses or use inaccurate pose uncertainty approximation, which may lead to deviation or even failure when exploring prior unknown environments. In this paper, we explicitly consider full-state (pose & map) uncertainty for balancing exploration and localizability, i.e., avoiding the robot guiding itself to complex scenes with high exploration rewards but hard to localize. We first propose a Rao-Blackwellized particle filter-based localization and mapping framework (RBPF-CLAM) for a dense environmental map with continuous occupancy distribution. Then we develop a new closed-form particle weighting method to improve the localization accuracy and robustness. We further use these weighted particles to approximate the unknown pose uncertainty and combine it with our previous confidence-rich mutual information (CRMI) metric to evaluate the expected information utility of the robot’s new control actions. This new information metric is called uncertain CRMI (UCRMI). Dataset experiments show our RBPF-CLAM improves about 44.7% average root mean square error than the state-of-the-art RBPF localization method, and real-world experimental results show that our UCRMI reduces the pose uncertainty about 32.85% more than CRMI and 25.36% time cost than UGPVR in the exploration of unknown and unstructured scenes given sparse measurements, which shows better performance than other state-of-the-art information metrics. Note to Practitioners—This work was motivated by the problem of ‘planning for state estimation’ for a range-sensing robot, i.e., the robot can choose a better future place to facilitate its localization more accurately and explore new areas rationally to gather more information. Existing methods mainly assume the robot’s poses during the exploration can be estimated by an independent localization approach or simply propagated via a predefined probabilistic distribution. However, localization failure would lead to higher planning deviation for the planner that does not consider the pose uncertainty, and manually set parametric distribution is more prone to overestimate the pose uncertainty. This paper proposes an RBPF-based localization and mapping scheme and an improved particle weight update method in a confidence-rich map, then uses the weighted particles to approximate trajectory entropy and combines it with CRMI to evaluate the expected information gain of a candidate action/node. Our newly defined information function ‘UCRMI’ can prevent the robot from exploring too aggressively without considering its localizability in prior unknown and unstructured environments. These scenes may lack robust features to conduct feature-based SLAM or lack accurate external localization information such as GPS. This method can be applied in underwater, planetary, and subterranean robot exploration tasks, even using low-resolution sensors. Future work mainly involves adapting UCRMI to applications in large-scale scenes using small autonomous platforms.

I. Introduction

Autonomous robot exploration and active simultaneous localization and mapping (SLAM) have played important roles in different exploration tasks of unknown environments such as subterranean caves, exoplanets, maritime activities, etc [1], [2], [3], [4], and [5]. Early exploration methods such as the probabilistic roadmap (PRM) [6], the rapidly-exploring random trees (RRT) [7], and their derivatives (e.g., RRT*/PRM* [8], Neural RRT* [9]) aimed to explore an interested area rapidly to find a near-optimal/optimal path given kinodynamic constraints of the robot or prior map information.