利用历史数据冗余，实现自动驾驶激光雷达数据210倍压缩
Been There, Scanned That: Nostalgia-Driven LiDAR Compression for Self-Driving Cars
- 通过对比历史点云差异实现跨日、跨月数据压缩
- 在15厘米重建误差下实现210倍压缩率
- 适合长期运行的自动驾驶车队数据存储
自动驾驶车辆每天可产生数TB传感器数据，其中大量为激光雷达生成的3D点云。这些数据需传输至云端用于模型训练或事故分析。为降低网络与存储成本，本文提出DejaView。现有方法仅利用帧间冗余，而DejaView则挖掘更大时间尺度（天、月）的冗余。基于自动驾驶车辆活动区域有限且每日路线重复的特性，其核心是差分操作，将当前点云表示为与历史数据的增量。在两个月的激光雷达数据上，端到端实现210倍压缩，重建误差仅15厘米。
原文摘要 · Abstract (English)
An autonomous vehicle can generate several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDARs. This data must be transferred to cloud storage, where it is utilized for training machine learning models or conducting analyses, such as forensic investigations in the event of an accident. To reduce network and storage costs, this paper introduces DejaView. Although prior work uses interframe redundancies to compress data, DejaView searches for and uses redundancies on larger temporal scales (days and months) for more effective compression. We designed DejaView with the insight that the operating area of autonomous vehicles is limited and that vehicles mostly traverse the same routes daily. Consequently, the 3D data they collect daily is likely similar to the data they have captured in the past. To capture this, the core of DejaView is a diff operation that compactly represents point clouds as delta w.r.t. 3D data from the past. Using two months of LiDAR data, an end-to-end implementation of DejaView can compress point clouds by a factor of 210 at a reconstruction error of only 15 cm.
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