Point cloud noise is inevitable in the LiDAR scanning of objects and affects measurement accuracy and integrity. To minimize such noise, we propose a gravitational feature function-based point cloud denoising algorithm and a universal gravitation formula for a point cloud. First, we calculate the point cloud barycenter (i.e., the position of the average mass distribution) and the spherical neighborhood of points in terms of the distribution of the point cloud in three-dimensional space. Next, using the proposed formula, we calculate the gravitational forces between the barycenter and the spherical neighborhood of all points. We then combine all of the gravitational forces into a gravitational feature function and filter the noises in the point cloud using a gravitational feature-function threshold. This novel algorithm, to the best of our knowledge, effectively removes drift noises and takes into account the local and global structure of point clouds. Finally, we demonstrate the effectiveness of the algorithm through extensive experiments in which sparse, dense, and mixed noises are removed.

LiDAR point cloud object recognition plays an important role in robotics, remote sensing, and automatic driving. However, it is difficult to fully represent the object feature information only by using the point cloud information. To address this challenge, we proposed a point cloud object recognition method that uses intensity image compensation, which is highly descriptive and computationally efficient. First, we constructed the local reference frame for the point cloud. Second, we proposed a method to calculate the deviation angle between the normal vector and local reference frame in the local neighborhood of the point cloud. Third, we extracted the contour information of the object from the intensity image corresponding to the point cloud, carried out Discrete Fourier Transform on the distance sequence between the barycenter of the contour and each point of the contour, and took the obtained result as Discrete Fourier Transform contour feature of the object. Finally, we repeated the above steps for the existing prior data and marked the obtained results as the feature information of the corresponding object to build a model library. We can recognize an unknown object by calculating the feature information of the object to be recognized and matching the feature information with the model library. We rigorously tested the proposed method with avalanche photon diode array LiDAR data and compared the results with those of four other methods. The experimental results show that the proposed method is superior to the comparison method in terms of description and computational efficiency and that it can meet the needs of practical applications.

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