Detecting people in sprinting motion using HPRDenoise: Point cloud denoising with hidden point removal

Taku Itami, Yuki Takeyama, Sota Akamine, Jun Yoneyema, Sebastien Ibarboure


LiDARs are utilized in various applications, such as self-driving vehicles and robotics, to aid in sensing the environment. However, LiDARs do not provide instantaneous images and they generate noise, adding to measurement errors. This noise, often referred to as motion blur phenomenon also observed in other imaging sensors results in decreased sensing accuracy for moving objects. This study introduces HPRDenoise, a noise reduction method based on hidden point removal, specifically designed to reduce motion blur during sprinting motion. This method capitalizes on the occlusion produced by a fixed-position LiDAR. We propose a comprehensive denoising approach to filter points from a point cloud without resorting to supervised learning, unlike most existing denoising algorithms. The number of correct frames and accuracy were compared for Raw, ScoreDenoise, which is the state-of-the-art method for random point cloud denoising, and HPRDenoise (Ours). Accuracy is defined as the ratio of the number of correct frames to the total number of frames. Experimental results demonstrate that the detection accuracy of point clouds processed with HPRDenoise is 72.73%, achieving better accuracy than those using conventional methods.


LiDAR; motion blur; noise reduction; hidden point removal

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