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Building a LiDAR Obstacle Detector in C++
- This project focuses on processing Lidar point cloud data in urban driving scenarios, explaining Lidar's principles and advantages over other sensors like cameras and radar.
- LiDAR provides accurate 3D measurements by emitting laser pulses to calculate distances, even in poor lighting conditions, and generates high-resolution maps of surroundings.
- The project processes raw point cloud data (in .pcd files) using filtering, segmentation, and clustering techniques for obstacle detection in highway driving scenarios.
- Lidar data is transformed into structured obstacle detections with bounding boxes, essential for path planning, collision avoidance, and navigation.
- The article details the steps involved in filtering, segmenting, and clustering Lidar data, enhancing understanding of obstacle detection in autonomous systems.
- Various methods like Voxel Grid Filtering and Euclidean clustering are employed to process and group point cloud data efficiently for obstacle detection.
- RANSAC algorithm is utilized to separate road points from obstacles, aiding path planning by identifying road surfaces and potential hazards.
- KD-Tree data structure improves nearest neighbor search efficiency, essential for clustering points in obstacle detection scenarios.
- Euclidean clustering algorithm groups points based on proximity, facilitating the identification and labeling of different objects like cars or pedestrians.
- Implementing bounding boxes around clustered points further enhances obstacle detection and path planning by defining restricted areas to avoid collisions.
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