Autonomous driving technologies enable motor vehicles to drive themselves safely and reliably, and are being widely researched for smart cities and urban services [1]. The ability to perceive their surroundings is essential for unmanned ground vehicles (UGVs) to achieve autonomous driving [2]. Autonomous UGVs need to obtain a large amount of accurate environmental data to support automatic object avoidance and local path planning [3]. Several types of environment sensors, such as fisheye, binocular, and depth cameras, are widely used to obtain real-time information about a vehicle's surroundings so it can be aware of its environment [4-6]. Compared with these, the clearest advantage of Light Detection and Ranging (LiDAR) is that it can rapidly collect high-precision, wide-range point clouds [7]. Classifying and recognizing the features of individual objects based on these point clouds is a crucial challenge, and involves exploiting their unique properties,
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