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.
By Chunhao Shi, et al China.
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.
In this paper, we propose a LiDAR point cloud object recognition method using intensity image compensation. The proposed method does not require cross-spectral image registration, and the added intensity information improves the description of the algorithm. We tested the performance of the proposed method with the APD array LiDAR data. The experimental results show that the RPC curve of the proposed method is better than that of the comparison methods, and the average computing time of the proposed method is 0.0699 s, which is better than the comparison methods. In summary, it can be seen that the proposed method is highly descriptive and computationally efficient.
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