FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments

TL;DR

This paper introduces FIT-SLAM, an active SLAM approach for ground robots in 3D environments that balances exploration efficiency with localization accuracy by integrating Fisher Information and traversability estimation.

Contribution

It presents a novel exploration method combining traversability mapping, goal selection, and path planning that leverages SLAM landmark information for improved robustness.

Findings

Significant increase in exploration rate compared to existing methods.

Effective minimization of localization covariance during exploration.

Validated in both simulated and real-world 3D environments.

Abstract

Active visual SLAM finds a wide array of applications in GNSS-Denied sub-terrain environments and outdoor environments for ground robots. To achieve robust localization and mapping accuracy, it is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. Through this work, we propose FIT-SLAM (Fisher Information and Traversability estimation-based Active SLAM), a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments. This approach is devised with the dual objectives of sustaining an efficient exploration rate while optimizing SLAM accuracy. Initially, an estimation of a global traversability map is conducted, which accounts for the environmental constraints pertaining to traversability. Subsequently, we propose a goal candidate selection approach along with a path planning method towards this goal that takes into account the information provided by the landmarks used by the SLAM backend to achieve robust localization and successful path execution . The entire algorithm is tested and evaluated first in a simulated 3D world, followed by a real-world environment and is compared to pre-existing exploration methods. The results obtained during this evaluation demonstrate a significant increase in the exploration rate while effectively minimizing the localization covariance.