Object-Aware View Planning for Autonomous 3-D Model Reconstruction of Buildings Using a Mobile Robot
Yuxiang Li, Jiancheng Wang, Haoyao Chen, Xin Jiang, Yunhui Liu
Abstract
Active mapping ranks among the most critical applications of autonomous mobile robots. In this article, we focus on 3D model reconstruction of buildings using an outdoor ground robot equipped with a solid-state LiDAR and a gimbal. A view-planning approach is proposed to generate and evaluate view sequences with reference to the characteristics of solid-state LiDAR and the robot configuration. To reconstruct the specific buildings while ignoring unrelated objects in the environment, we introduce object awareness into the map representation, and frontier- and sampling-based strategies are combined to plan the view sequences for the effective observation of a target building. Instead of greedily maximizing information gain, frontier clusters indicating the mapped object’s incomplete surfaces are used to evaluate the sampled views, and sampling the minimum view set generates view sequences able to efficiently cover the clusters. Moreover, using object awareness to partition the area of sampled views around the building facilitates a divide-and-conquer strategy that overcomes local characteristics and guides a complete reconstruction. Simulation and real-world experiments revealed that our approach allows the thorough and efficient active reconstruction of various buildings and even outperforms state-of-the-art approaches.