Natural corners-based two-dimensional (2D) SLAM with partial compatibility algorithm in indoor environment

This paper presents a natural corners-based two-dimensional (2D) Simultaneous Localization and Mapping (SLAM) with a robust data association algorithm in a real unknown environment. The corners are extracted from raw laser sensor data and chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, the local best matching vector between the new corners and the stored ones is found by joint compatibility, while the nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with the linear matching time. The SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.