Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously Optimizing Robot Poses and Occupancy Map

TL;DR

Occupancy-SLAM introduces a joint optimization approach for robot poses and occupancy maps, improving accuracy and robustness in SLAM tasks by optimizing both simultaneously, unlike traditional methods.

Contribution

The paper presents a novel optimization-based SLAM method that jointly optimizes robot trajectory and occupancy map, including both pose and occupancy values, in a unified framework.

Findings

Achieves more accurate robot trajectories and occupancy maps than state-of-the-art methods.

Demonstrates robustness and efficiency through simulations and real datasets.

Preliminary 3D results show promising potential for practical applications.

Abstract

Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations because they effectively classify spaces into obstacles, free areas, and unknown regions, providing robots with spatial information for various tasks. In this paper, we propose Occupancy-SLAM, a novel optimization-based SLAM method that enables the joint optimization of robot trajectory and the occupancy map through a parameterized map representation. The key novelty lies in optimizing both robot poses and occupancy values at different cell vertices simultaneously, a significant departure from existing methods where the robot poses need to be optimized first before the map can be estimated. Evaluations using simulations and practical 2D laser datasets demonstrate that the proposed approach can robustly obtain more accurate robot trajectories and occupancy maps than state-of-the-art techniques with comparable computational time. Preliminary results in the 3D case further confirm the potential of the proposed method in practical 3D applications, achieving more accurate results than existing methods.