Robust real-time detection and motion forecasting of traffic participants are necessary for autonomous vehicles to safely navigate urban environments. We present RV-FuseNet, a novel end-to-end approach for joint detection and trajectory estimation using raw time-series LiDAR data. Instead of the widely used bird's eye view (BEV) representation, we utilize the native range view (RV) representation of LiDAR data. RV preserves the full resolution of the raw sensor data by avoiding the voxelization used in BEV. Furthermore, RV can be processed efficiently due to its compactness. However, for time-series fusion, the data is projected to a common viewpoint, and often this viewpoint is different from where it was captured. This can lead to loss of data and structure in RV which has an adverse impact on performance. To address this challenge we propose a novel architecture that sequentially projects each RV sweep into the viewpoint of the next sweep in time. We demonstrate that our sequential fusion approach is superior to directly projecting all the data into the most recent viewpoint. Furthermore, we show that our approach significantly improves motion forecasting accuracy over the existing state-of-the-art.

中文翻译：

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RV-FuseNet：基于范围视图的时间序列激光雷达数据融合，用于联合 3D 对象检测和运动预测

交通参与者的稳健实时检测和运动预测对于自动驾驶汽车安全地在城市环境中导航是必要的。我们提出了 RV-FuseNet，这是一种使用原始时间序列 LiDAR 数据进行联合检测和轨迹估计的新型端到端方法。我们没有使用广泛使用的鸟瞰图 (BEV) 表示，而是利用 LiDAR 数据的本地范围视图 (RV) 表示。RV 通过避免 BEV 中使用的体素化来保留原始传感器数据的完整分辨率。此外，由于其紧凑性，可以有效地处理 RV。然而，对于时间序列融合，数据被投影到一个共同的视点，这个视点通常与它被捕获的位置不同。这可能会导致 RV 中的数据和结构丢失，从而对性能产生不利影响。为了应对这一挑战，我们提出了一种新颖的架构，该架构将每个 RV 扫描按时间顺序投影到下一次扫描的视点中。我们证明了我们的顺序融合方法优于直接将所有数据投影到最新的观点。此外，我们表明我们的方法显着提高了现有最先进技术的运动预测准确性。