论文标题
通过在线拓扑和几何路径优化的自动户外扫描
Autonomous Outdoor Scanning via Online Topological and Geometric Path Optimization
论文作者
论文摘要
自主3D获取户外环境带来了特殊的挑战。不同于室内场景，那里的房间空间被清晰的边界和分离所描绘（例如墙壁和家具），室外环境宽敞且无限制（考虑一个校园）。因此，与扫描努力主要用于发现边界表面的室内场景不同，扫描开放且无界区域需要积极地划定扫描区域的程度并在该区域内动态规划遍历路径。因此，对于室外场景，我们通过对机器人扫描路径的离散优化的优化制定节能自主扫描的计划。离散优化通过解决在线旅行销售问题（在线TSP）来计算拓扑图，该问题决定了扫描目标和途径。动态目标被确定为访问网站的集合，并获得了尚无可见性的奖励。访问图是通过连接访问站点的边缘通过遍历成本加权的边缘来构建的。随着机器人扫描通过删除被访问或变得无奖励并插入新发现的网站而扫描的拓扑地图。连续部分通过最大化沿路径扫描的信息增益在两个相邻访问位点之间几何优化遍历路径。离散和连续的过程交流，直到当前图的遍历成本超过机器人的剩余能量。通过合成和现场测试对我们的方法进行了评估，证明了其有效性和优势比替代方案。 The project is at http://vcc.szu.edu.cn/research/2020/Husky, and the codes are available at https://github.com/alualu628628/Autonomous-Outdoor-Scanning-via-Online-Topological-and-Geometric-Path-Optimization.
Autonomous 3D acquisition of outdoor environments poses special challenges. Different from indoor scenes, where the room space is delineated by clear boundaries and separations (e.g., walls and furniture), an outdoor environment is spacious and unbounded (thinking of a campus). Therefore, unlike for indoor scenes where the scanning effort is mainly devoted to the discovery of boundary surfaces, scanning an open and unbounded area requires actively delimiting the extent of scanning region and dynamically planning a traverse path within that region. Thus, for outdoor scenes, we formulate the planning of an energy-efficient autonomous scanning through a discrete-continuous optimization of robot scanning paths. The discrete optimization computes a topological map, through solving an online traveling sales problem (Online TSP), which determines the scanning goals and paths on-the-fly. The dynamic goals are determined as a collection of visit sites with high reward of visibility-to-unknown. A visit graph is constructed via connecting the visit sites with edges weighted by traversing cost. This topological map evolves as the robot scans via deleting outdated sites that are either visited or become rewardless and inserting newly discovered ones. The continuous part optimizes the traverse paths geometrically between two neighboring visit sites via maximizing the information gain of scanning along the paths. The discrete and continuous processes alternate until the traverse cost of the current graph exceeds the remaining energy capacity of the robot. Our approach is evaluated with both synthetic and field tests, demonstrating its effectiveness and advantages over alternatives. The project is at http://vcc.szu.edu.cn/research/2020/Husky, and the codes are available at https://github.com/alualu628628/Autonomous-Outdoor-Scanning-via-Online-Topological-and-Geometric-Path-Optimization.
