Robotic Online Path Planning on Point Cloud
Robotic Online Path Planning on Point Cloud
复制标题
点云上的机器人在线路径规划
DOI：
10.1109/tcyb.2015.2430526
复制
发表时间：
2016-05-01
影响因子：
11.8
通讯作者：
Liu, Ming
中科院分区：
文献类型：
--
作者：
Liu, Ming
研究了移动的轮式或履带式机器人在2.5维环境中的路径规划问题，其中可通过曲面通常被认为是嵌入在3维环境空间中的2维流形。特别是，我们的目标是解决2.5-D导航问题，使用原始点云作为输入。所提出的方法是独立于传统的表面参数化或重建方法，如网格化过程，这通常具有高计算复杂度。相反，我们利用原始点云上的3-D张量投票框架的输出。通过在图形计算单元上的优化实现，加快了张量投票的计算速度。基于张量投票的结果，一个新的局部黎曼度量定义的显着性成分，这有助于潜在的可遍历表面的建模。使用所提出的度量，我们证明了测地线在3-D张量空间导致合理的路径规划结果的实验。与传统的方法相比，结果表明，该方法的优势，平滑的机器人机动，同时考虑最小的行程距离。
This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.