Cooperative perception is challenging for safety-critical autonomous driving applications.The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles. To correct the noisy relative transform, the local measurements of both connected vehicles (bounding boxes) are utilized, and an optimal transport theory-based algorithm is developed to filter out those objects jointly detected by the vehicles along with their correspondence, constructing an associated co-visible set. A correction transform is estimated from the matched object pairs and further applied to the noisy relative transform, followed by global fusion and dynamic mapping. Experiment results show that robust performance is achieved for different levels of location and heading errors, and the proposed framework outperforms the state-of-the-art benchmark fusion schemes, including early, late, and intermediate fusion, on average precision by a large margin when location and/or heading errors occur.

翻译：协同感知对于安全关键的自动驾驶应用具有挑战性。共享位置和位姿中的误差会导致不准确的相对变换估计，并破坏自车的鲁棒建图。我们提出了一种名为OptiMatch的分布式对象级协同感知系统，其中检测到的3D边界框和局部状态信息在联网车辆之间共享。为了修正带噪声的相对变换，利用了两辆联网车辆（边界框）的局部测量值，并开发了一种基于最优传输理论的算法，用于筛选出车辆共同检测到的对象及其对应关系，从而构建关联的共视集合。从匹配的对象对中估计出一个修正变换，并将其进一步应用于带噪声的相对变换，随后进行全局融合和动态建图。实验结果表明，该方法在不同的位置和航向误差水平下均实现了鲁棒性能，并且所提出的框架在发生位置和/或航向误差时，其平均精度大幅优于包括早期融合、后期融合和中间融合在内的最先进基准融合方案。