Accurate Localization Algorithm for Indoor Robots Using Branch and Bound Strategy

Abstract

Accurate localization is a prerequisite for robots to realize autonomous navigation. This paper proposes a robot localization algorithm suitable for indoor environments to effectively solve the problem of global localization and position tracking. First, global localization is realized by employing a two-stage matching method involving coarse and fine levels based on a branch-and-bound algorithm and iterative nearest point (ICP) algorithm to estimate the initial pose of the robot. Second, a local map-based scan matching method is used to realize position tracking. To address the accumulated errors between local maps, we build a pose graph to achieve loop-closure optimization. Moreover, to decrease the computational complexity of loop-closure detection, the branch-and-bound algorithm is used to accelerate the search of the loop constraints. The performance of the proposed algorithm is comprehensively evaluated in real application scenarios using commercial logistics robots. The experimental results demonstrate that represents a highly accurate and robust localization algorithm.