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Security robots often operate in environments characterized by low light and smoke, where millimeter-wave radar proves effective. However, the millimeter-wave radar's point cloud is often sparse and noisy, potentially leading to positioning failure when employing point cloud matching. In this paper, we propose a localization strategy for security robots based on millimeter wave radar. The position of the security robot is deduced by clustering and tracking the sparse point cloud. In addition, radial velocity was used to design an adaptive tracking threshold, so that the targets in the two frames of data are within the tracking threshold regardless of the fast or slow motion speed of the security robot. Experimental results indicate that this method circumvents positioning failures associated with point cloud matching. In comparison to the radial velocity method, this approach enhances positioning accuracy by approximately 33.9%. Additionally, compared to the fixed tracking association threshold, this method exhibits a higher success rate in target tracking, leading to a 25.9% improvement in security robot positioning accuracy.
Dai et al. (2024) studied this question.