A novel and an efficient rescue system with a multi-agent simultaneous localization and mapping (SLAM) framework is proposed to reduce the rescue time while rescuing the people trapped inside a burning building. In this study, the truncated signed distance (TSD) based SLAM algorithm is employed to accurately construct a two-dimensional map of the surroundings. For a new and significantly different scenario, information is gathered and the general iterative closest point method (GICP) is directly employed instead of the conventional TSD-SLAM process. Rescuers can utilize a total map created by merging individual maps, allowing them to efficiently search for victims. For online map merging, it is essential to determine the timing of when the individual maps are merged and the extent to which one map reflects the other map, via the weights. In the several experiments conducted, a light-detection and ranging system and an inertial measurement unit were integrated into a smart helmet for rescuers. The results indicated that the map was built more accurately than that obtained using the conventional TSD-SLAM. Additionally, the merged map was built more correctly by determining proper parameters for online map merging. Consequently, the accurate merged map allows rescuers to search for victims efficiently.

This Letter presents a method that reconstructs 3D object using camera clustering and key camera selection to resolve scalability problem. To perform camera clustering, camera similarity is defined using geodesic distance and overlap constraint between cameras. Key cameras are then selected to reconstruct an object considering overlapping cameras and high curvature regions. As a result, it is achievable to relax increases in the execution time and the accumulated errors due to the scalability problems with a large number of cameras.

In this paper, we address a 3D reconstruction problem using depth prediction from a single RGB image. With the recent advances in deep learning, depth prediction shows high performance. However, due to the discrepancy between training environment and test environment, 3D reconstruction can be vulnerable to the uncertainty of depth prediction. To consider the uncertainty of depth prediction for robust 3D reconstruction, we adopt Bayesian deep learning framework. Conventional Bayesian deep learning requires a large amount of time and GPU memory to perform Monte Carlo sampling. To address this problem, we propose a lightweight Bayesian neural network consisting of U-net structure and summation-based skip connections, which is performed in real-time. Estimated uncertainty is utilized in probabilistic TSDF fusion for dense 3D reconstruction by maximizing the posterior of TSDF value per voxel. As a result, global TSDF robust to erroneous depth values can be obtained and then dense 3D reconstruction from the global TSDF is achievable more accurately. To evaluate the performance of depth prediction and 3D reconstruction using our method, we utilized two official datasets and demonstrated the outperformance of the proposed method over other conventional methods.

Scan similarity-based pose graph construction method for graph SLAM is proposed. To perform delicate pose graph SLAM, front-end that constructs a graph as well as back-end that optimizes the constructed graph is an important task. Generally, there is an error accumulation phenomenon during the odometry estimation process. This paper focuses on the method of creating a high quality graph by suggesting ways to improve the graph accuracy since the accumulated errors in the graph might degrade the performance of the entire graph SLAM. We deal with one of our previous works, dynamic keyframe selection technique, based on scan similarity computation method more precisely and suggest a loop closure detection method by exploiting previously proposed 2-D laser scan descriptor. To verify objective performance of the proposed method, the experimental results of the odometry estimation are shown by using the benchmark dataset and the real world dataset. Additionally, results of the pose graph SLAM are shown for the real world dataset which include the loop clorues.