A Robust 2D Lidar SLAM Method in Complex Environment

The two-dimensional(2D)lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment.We propose a robust 2D lidar simultaneous localization and mapping(SLAM)algorithm working in ambiguous environments.To improve the front-end scan-matching module’s accuracy and robustness,we propose performing degeneration analysis,line landmark tracking,and environment coverage analysis.The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment.Moreover,we propose a tightly coupled framework that integrates lidar,wheel odometer,and inertial measurement unit(IMU).The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem.The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8%of the existing method.And it can run stably in ambiguous environments where the existing method will be failed.Moreover,it ccan successfully construct a map with an area of more than 250000 square meters.

Fast Four-Stage Local Motion Planning Method for Mobile Robot

Mobile robot local motion planning is responsible for the fast and smooth obstacle avoidance,which is one of the main indicators for evaluating mobile robots'navigation capabilities.Current methods formulate local motion planning as a unified problem;therefore it cannot satisfy the real-time requirement on the platform with limited computing ability.In order to solve this problem,this paper proposes a fast local motion planning method that can reach a planning frequency of 500 Hz on a low-cost CPU.The proposed method decouples the local motion planning as the front-end path searching and the back-end optimization.The front-end is composed of the environment topology analysis and graph searching.The back-end includes dynamically feasible trajectory generation and optimal trajectory selection.Different from the popular methods,the proposed method decomposes the local motion planning into four sub-modules,each of which aims to solve one problem.Combining four sub-modules,the proposed method can obtain the complete local motion planning algorithm which can fast generate a smooth and collision-free trajectory.The experimental results demonstrate that the proposed method has the ability to obtain the smooth,dynamically feasible and collision-free trajectory and the speed of the planning is fast.