Pedestrian Detection and Tracking Based on 2D Lidar

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In order to make the service robot carry out dynamic path planning according to the movement state of pedestrians and improve the ability of collaboration with humans, this paper proposes a pedestrian detection and tracking algorithm based on 2D lidar. Pedestrian detection based on 2D lidar is mainly done by detecting the legs of pedestrians. In the laser data preprocessing stage, in order to reduce the influence of lidar point cloud noise, Gaussian filtering is used to filter the original point cloud data. In order to reduce the influence of the environment itself on the detection accuracy, an algorithm for projecting the laser data with the static map is proposed to remove the static background data. In the clustering segmentation stage, this paper uses Euclidean clustering to segment the laser point cloud clustering. In the feature extraction stage, in order to improve the detection accuracy of pedestrians' legs, the number of statistical features is increased on the basis of extracting geometric features; In order to improve the generalization ability of the model, and to solve the problem of scale inconsistency of pedestrian leg feature description, the extracted pedestrian features are normalized. This article uses SVM to build a human leg classifier. In the pedestrian tracking phase, this paper proposes a multi-person tracking algorithm based on Kalman filtering. The experimental results show that the proposed scheme can obtain better pedestrian detection and tracking effects, and the real-time performance is higher.