Scan planning and route optimization for control of execution of as-designed BIM

Abstract. Scan-to-BIM systems have been recently proposed for the dimensional and quality assessment of as-built construction components with planned works. The procedure is generally based on the geometric alignment and comparison of as-built laser scans with as-designed BIM models. A major concern in Scan-to-BIM procedures is point cloud quality in terms of data completeness and consequently, the scanning process should be designed in order to obtain a full coverage of the scene while avoiding major occlusions. This work proposes a method to optimize the number and scan positions for Scan-to-BIM procedures following stop & go scanning. The method is based on a visibility analysis using a ray-tracing algorithm. In addition, the optimal route between scan positions is formulated as a travelling salesman problem and solved using a suboptimal ant colony optimization algorithm. The distribution of candidate positions follows a grid-based structure, although other distributions based on triangulation or tessellation can be implemented to reduce the number of candidate positions and processing time.Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016- 038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158- C4-2-RMinisterio de Economia, Industria y Competitividad | Ref. RTC-2016-5257-

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D map

This paper presents an efficient and reliable algorithm for autonomous robots to build large-scale three dimensional maps by combining small local submaps. The algorithm is a generalization of our recent work on two dimensional map joining algorithm - Iterated Sparse Local Submap Joining Filter (I-SLSJF). The 3D local submap joining problem is formulated as a least squares optimization problem and solved by Extended Information Filter (EIF) together with smoothing and iterations. The resulting information matrix is exactly sparse and this makes the algorithm efficient. The smoothing and iteration steps improve the accuracy and consistency of the estimate. The consistency and efficiency of 3D I-SLSJF is demonstrated by comparing the algorithm with some existing algorithms using computer simulations. ©2009 IEEE

SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention

This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model's performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system's behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters.Comment: International Conference on Advanced Robotics (ICAR 2023

SEM-GAT: explainable semantic pose estimation using learned graph attention

This paper proposes a Graph Neural Network (GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model’s performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system’s behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters