A Robust Localization System Fusion Vision-CNN Relocalization and Progressive Scan Matching for Indoor Mobile Robots

Abstract

Map-based, high-precision dynamic pose tracking and rapid relocalization in the case of unknown poses are very important for indoor navigation robots. This paper aims to propose a robust and high-precision indoor robot positioning algorithm that combines vision and laser sensor information. This algorithm mainly includes two parts: initialization and real-time pose tracking. The initialization component is mainly to solve the problem of the uncertainty of a robot’s initial pose and loss of pose tracking. First, the laser information is added to the posenetLSTM neural network that only considers image information as a geometric constraint, and the loss function is redesigned thereby improving global positioning accuracy. Second, on the basis of visual rough positioning, the branch and bound method is used to quickly search the high-precision pose of the robot. In the real-time tracking component, small-scale correlation sampling is performed on the high-resolution environment grid map, and the robot’s pose is dynamically tracked in real time. When the score of the tracking pose is lower than a certain threshold, the method of nonlinear graph optimization is used to perform the pose optimization. In order to prove the robustness, high precision, and real-time performance of the algorithm, this article first builds a simulation environment in Gazebo for evaluation, and then verifies the relevant performance of the algorithm through the Mir robot platform. Both simulations and experiments show that the introduction of laser information into the neural network can greatly improve the accuracy of vision relocalization and the system can quickly perform high-precision repositioning when the camera is not severely blocked. At the same time, compared with the pose tracking performance of the adaptive Monte Carlo localization (AMCL) algorithm, the proposed algorithm has also improved in accuracy and in real-time performance.