论文标题
主动绘制工业结构，并在四倍的机器人上进行基于信息增益的计划
Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot
论文作者
论文摘要
在本文中，我们开发了一个在线主动映射系统，以使一个四倍的机器人自主调查大型物理结构。我们描述了扫描和重建感兴趣的对象所需的感知，计划和控制模块，而无需先前的模型。该系统构建对象的体素表示，并根据重建本身并避免与环境发生冲突，从而迭代地确定下一最佳视图（NBV）以扩展表示形式。通过计算一组在Assensed Terrain Map上采样的候选扫描位置的预期信息获得，以及到达这些候选人的成本，该机器人决定NBV以进行进一步探索。机器人计划通往NBV的最佳途径，避免障碍和不可探测的地形。模拟环境和现实环境的实验结果都显示了我们系统的能力和效率。最后，我们在真正的机器人，Anybotics Anymal上进行了完整的系统演示，自主重建建筑物的立面和工业结构。
In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure.
