Spinning LiDAR data are prevalent for 3D perception tasks, yet its cylindrical image form is less studied. Conventional approaches regard scans as point clouds, and they either rely on expensive Euclidean 3D nearest neighbor search for data association or depend on projected range images for further processing. We revisit the LiDAR scan formation and present a cylindrical range image representation for data from raw scans, equipped with an efficient calibrated spherical projective model. With our formulation, we 1) collect a large dataset of LiDAR data consisting of both indoor and outdoor sequences accompanied with pseudo-ground truth poses; 2) evaluate the projective and conventional registration approaches on the sequences with both synthetic and real-world transformations; 3) transfer state-of-the-art RGB-D algorithms to LiDAR that runs up to 180 Hz for registration and 150 Hz for dense reconstruction. The dataset and tools will be released.
翻译:3D 感知任务通常使用LiDAR 旋转图象数据,但其圆柱形图象形式研究较少。 常规方法将扫描视为点云,它们或者依靠昂贵的Euclidean 3D近邻搜索数据协会,或者依靠预测的射程图象进行进一步处理。 我们重新研究LiDAR 扫描形成,并为原始扫描数据提供一个圆柱形图象显示器,配有高效校准球形投影模型。 我们的配方,我们 (1) 收集大型的LiDAR数据数据集,由室内和室外序列组成,并配有假地面真象外序列;(2) 评价合成和现实世界变异序列的投影和常规登记方法;(3) 将最先进的RGB-D算法转移到LiDAR,该算法将多达180赫兹用于登记,150赫兹用于密集重建。 数据集和工具将被释放。