(SEG-TEST#02) Ground Segmentation Tests (V 1.0.0): KITTI Data | Optimized CUDA RANSAC

GitHub Repository:
https://github.com/MengWoods/enhanced-RANSAC-ground-segmentation

This video demonstrates a major update to the enhanced RANSAC ground segmentation project, showcasing significant improvements in speed, stability, and accuracy.

The previous version struggled in complex city and residential scenes. As you can see in this test, those issues have been resolved, and the algorithm now performs robustly across all tested KITTI dataset sequences.

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Key Improvements in This Version:

1. Massive Speed Optimization: The core RANSAC CUDA kernel was re-architected to use a parallel-collaborative approach. This reduced the main loop processing time from 50-150ms down to consistently under 10ms per frame—a greater than 10x speedup!

2. Increased Accuracy & Reliability: Thanks to the huge performance gain, the number of RANSAC iterations was increased from 40 to 500. This allows the algorithm to find a more optimal ground plane, dramatically improving the accuracy and reliability of the segmentation, especially in challenging scenes.

3. Stable Frequency Control: The main processing loop now runs at a controlled, consistent frequency, making the output suitable for real-time robotics applications.

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Test Scenarios & New Performance
The algorithm was tested on the same three KITTI sequences:

1. Road Scene (Highway/Rural - 2011_09_26_drive_0101): Remains highly stable and accurate. (0:01 min, 941 frames)

2. City Scene (Urban - 2011_09_29_drive_0071): The previous instability is gone. The segmentation is now consistent and correctly handles the complex urban geometry. (1:18 min, 1065 frames)

3. Residential Scene (Suburban - 2011_09_30_drive_0033): Now performs with excellent stability, correctly identifying the ground plane even in narrow and cluttered areas. (3:07 min, 1600 frames)

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This update successfully addresses the key bottlenecks of the initial version. The project is now a much more viable solution for real-time applications. The focus will continue on polishing the code and further refining the algorithm.

Thanks for watching!

#CUDA #PointCloud #LiDAR #GroundSegmentation #RANSAC #ComputerVision #Robotics #Optimization #C++ #Programming

Видео (SEG-TEST#02) Ground Segmentation Tests (V 1.0.0): KITTI Data | Optimized CUDA RANSAC канала Menghao Woods