Abstract
The abstract should briefly summarize the contents In the field of autonomous driving, autonomous cars need to perceive and understand their surroundings autonomously. At present, the most commonly used sensors in the field of driverless cars are RGB-D camera and LIDAR. Therefore, how to process the environmental information collected by these sensors, and extract the features we are interested in, and then use them to guide the driving of unmanned vehicles, has become an essential research point in the field of autonomous driving. Among them, compared with 2D image information, 3D point cloud can provide 3D orientation information of objects that 2D image does not have. Based on this, how to accurately process and perceive 3D point cloud and separate objects such as obstacles, cars and roads is crucial to the safety of autonomous driving. This paper adopts a method of preprocessing point cloud data and enhancing point cloud with images. KITTI dataset is the most classic and representative dataset in the field of autonomous driving. In the experiment, KITTI dataset is used to verify that this method can get good perception effect. In addition, we use the bird’s-eye view benchmark on KITTI to evaluate the performance of the original network. It is found that the improvement effect of this experiment on the original network is also very obvious.
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Huang, Q., Huang, J., Sheng, X., Yue, X. (2022). A Perception Method Based on Point Cloud Processing in Autonomous Driving. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_11
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DOI: https://doi.org/10.1007/978-981-19-6135-9_11
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