ERS: Faster LiDAR Point Cloud Registration for Connected Vehicles

The registration of LiDAR point clouds between Connected Vehicles (CV s) is the basis for vision tasks such as multi-vehicle cooperative perception and high-precision scene reconstruction. Due to the huge amount of LiDAR point cloud data and the complexity of computation, the registration has high transmission latency and execution time. Fast and accurate point cloud registration between CV s is urgently required by safe driving applications. To achieve faster registration, we propose partitioning overlapping point clouds for registration in advance. Meanwhile, for higher accuracy, we incorporate more geometric features during the registration. This paper demonstrates the Echo Registration System (ERS) developed to increase the speed of existing point cloud registration methods. ERS employs an initial positioning based on point cloud distributions, a density-consistent partition strategy, and a virtual geometric feature fusion. We deploy ERS to accelerate multiple state-of-the-art registration methods. The comprehensive results show that ERS improves the overall running time of current state-of-the-art baselines by 5.7 x with 42.1 % registration recall gains.