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About this paper
Chiyu Zhang, Ji Han, Yao Zou, Kexin Dong, Yujia Li, Junchun Ding, Xiaoling Han
Researchers at TuSimple developed a general, training-free quality metric to detect anomalous LiDAR pointcloud data, combining spatial autocorrelation and an intensity measure. The method effectively identifies noise caused by electromagnetic interference, rain, fog, and dust across different LiDAR sensor types, enabling real-time assessment of sensor health and efficient extraction of challenging data for autonomous driving system development.
Key insights
- Anomalies in LiDAR pointclouds, regardless of their source, often manifest as randomly dispersed points or points with abnormally low intensity values.
- Spatial autocorrelation can effectively quantify the dispersion versus clustering of LiDAR points, providing a robust indicator for noise patterns.
- An intensity-based weighting, when combined with spatial autocorrelation, enhances anomaly detection by capturing clustered noise (e.g., from heavy rain) that might otherwise appear coherent.
Summary by alphaXiv.
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