Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm

Abstract

The complexity of the environment limits the accuracy of the traditional Adaptive Monte Carlo Localization(AMCL) algorithm, which also suffers from high computational effort and particle degradation due to laser model limitations. To address these issues, an optimized AMCL algorithm with a bounding box is proposed. The AMCL algorithm is first parameterized and initialized to the particle swarm. During the particle iteration process, collision detection is performed on the bounding box. If a collision occurs, the particle filter is not updated and its particle weight is set to 1. If there is no collision, the particle filter is updated normally and the particle weight is set to 0. Then, the particles are resampled and updated based on the measurement data and motion model. After experimental verification, this method’s self-localization trajectory is closer to the actual path, and the measurement error fluctuation is smaller. The RVIZ simulation experiments revealed that the overall positioning time was optimized by 18.25% compared to the original AMCL, and by 9.28% compared to the improved AMCL. The optimization algorithm effectively improved the positioning accuracy and robustness of the system.

Keywords

• Adaptive monte carlo localization
• bounding box
• collision algorithm
• particle filter
• self-localization