3D LIDAR SEGMENTATION BASED ON EUCLIDEAN CLUSTERING FOR EMBEDDED SYSTEM
Abstract
This paper presents a method for 3D LiDAR segmentation based on Euclidean clustering specifically designed for embedded systems. LiDAR sensors are widely used for perception tasks in autonomous vehicles, robotics, and other applications, providing dense point cloud data of the environment. Segmentation of the point cloud into meaningful objects is essential for understanding the surroundings and making informed decisions. Euclidean clustering is an effective approach for grouping points based on spatial proximity, enabling object segmentation. However, implementing such algorithms on embedded systems poses challenges due to limited computational resources. In this work, we propose an optimized implementation of the Euclidean clustering algorithm tailored for embedded systems to achieve real-time performance on the embedded system. The proposed approach involves acquiring raw point cloud data from the LiDAR sensor and preprocessing it to reduce noise and size. Adaptive Euclidean clustering is then applied to group points into clusters based on their spatial proximity. Extracted features such as centroids and bounding boxes are utilized for object classification and segmentation. Post-processing steps refine the segmentation results, improving accuracy and removing spurious clusters
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