Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames

TL;DR

This paper introduces a grid-based submap joining algorithm for large-scale 2D SLAM that optimizes the global occupancy map and local submap frames efficiently, leveraging a pose-only Gauss-Newton method.

Contribution

The paper presents a novel formulation of the submap joining problem as a non-linear least squares problem and proves an independence property that enables a more efficient pose-only optimization algorithm.

Findings

Outperforms state-of-the-art methods in efficiency and accuracy

Effective in very large-scale environments

Validates with simulations and real datasets

Abstract

Optimizing robot poses and the map simultaneously has been shown to provide more accurate SLAM results. However, for non-feature based SLAM approaches, directly optimizing all the robot poses and the whole map will greatly increase the computational cost, making SLAM problems difficult to solve in large-scale environments. To solve the 2D non-feature based SLAM problem in large-scale environments more accurately and efficiently, we propose the grid-based submap joining method. Specifically, we first formulate the 2D grid-based submap joining problem as a non-linear least squares (NLLS) form to optimize the global occupancy map and local submap frames simultaneously. We then prove that in solving the NLLS problem using Gauss-Newton (GN) method, the increments of the poses in each iteration are independent of the occupancy values of the global occupancy map. Based on this property, we propose a poseonly GN algorithm equivalent to full GN method to solve the NLLS problem. The proposed submap joining algorithm is very efficient due to the independent property and the pose-only solution. Evaluations using simulations and publicly available practical 2D laser datasets confirm the outperformance of our proposed method compared to the state-of-the-art methods in terms of efficiency and accuracy, as well as the ability to solve the grid-based SLAM problem in very large-scale environments.