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

2024-10-14

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

In large-scale 2D non-feature-based SLAM, full-variable optimization suffers from high computational complexity and degraded accuracy. Method: This paper proposes a grid-based submap stitching framework. It formulates the problem as nonlinear least squares, represents the environment via occupancy grids, parameterizes submap poses, and jointly optimizes the global occupancy map and local submap poses. Contribution/Results: The key theoretical contribution is the first proof that, under Gauss–Newton optimization, pose increments are independent of occupancy grid values—enabling an equivalent algorithm that optimizes only submap poses, thereby bypassing the computational bottleneck of conventional joint optimization over both occupancy values and poses. Evaluated on synthetic and public LiDAR datasets, the method achieves real-time, high-precision mapping in ultra-large-scale environments and significantly outperforms state-of-the-art approaches.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

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 pose-only 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.

Problem

• 2D SLAM
• Featureless Environment
• Accuracy Degradation

Innovation

• Grid-based Subregion Stitching
• Nonlinear Least Squares Optimization
• Large-scale Environment SLAM