A robotic system for search and rescue missions needs to efficiently explore and map new areas. In this paper, we present an integrated exploration strategy with active loop closing, which balances between the exploration speed and map quality. Specifically, it finds a trade-off between moving towards unknown space to gather new information and revisiting previous locations to improve localization accuracy and map quality through loop closures. Our integrated exploration is built upon a submap-based 6D SLAM system. Loop closure constraints originate from pairwise submap matches, which allow the optimization of an underlying SLAM graph. During exploration, we employ the expected information gain as well as the robot's localization uncertainty estimates to weigh exploration and revisiting actions online. We introduce the match effect as the expected impact of a loop closure on global optimization and consider this novel criterion together with the match likelihood and cost when evaluating the utility of revisiting previous locations. To demonstrate our approach, we present simulated and real-world experiments, comparing two variants of our novel method to a frontier-based exploration.

Active scene exploration incorporates object recognition methods for analyzing a scene of partially known objects and exploration approaches for autonomous modeling of unknown parts. In this work, recognition, exploration, and planning methods are extended and combined in a single scene exploration system, enabling advanced techniques such as multi-view recognition from planned view positions and iterative recognition by integration of new objects from a scene. Here, a geometry based approach is used for recognition, i.e. matching objects from a database. Unknown objects are autonomously modeled and added to the recognition database. Next-Best-View planning is performed both for recognition and modeling. Moreover, 3D measurements are merged in a Probabilistic Voxel Space, which is utilized for planning collision free paths, minimal occlusion views, and verifying the poses of the recognized objects against all previous information. Experiments on an industrial robot with attached 3D sensors are shown for scenes with household and industrial objects.

We present a next-best-scan (NBS) planning approach for autonomous 3D modeling. The system successively completes a 3D model from complex shaped objects by iteratively selecting a NBS based on previously acquired data. For this purpose, new range data is accumulated in-the-loop into a 3D surface (streaming reconstruction) and new continuous scan paths along the estimated surface trend are generated. Further, the space around the object is explored using a probabilistic exploration approach that considers sensor uncertainty. This allows for collision free path planning in order to completely scan unknown objects. For each scan path, the expected information gain is determined and the best path is selected as NBS. The presented NBS approach is tested with a laser striper system, attached to an industrial robot. The results are compared to state-of-the-art next-best-view methods. Our results show promising performance with respect to completeness, quality and scan time.

The procedure of manually generating a 3D model of an object is very time consuming for a human operator. Next best-view (NBV) planning is an important aspect for automation of this procedure in a robotic environment. We propose a surface-based NBV approach, which creates a triangle surface from a real-time data stream and determines viewpoints similar to human intuition. Thereby, the boundaries in the surface are detected and a quadratic patch for each boundary is estimated. Then several viewpoint candidates are calculated, which look perpendicular to the surface and overlap with previous sensor data. A NBV is selected with the goal to fill areas which are occluded. This approach focuses on the completion of a 3D model of an unknown object. Thereby, the search space for the viewpoints is not restricted to a cylinder or sphere. Our NBV determination proves to be very fast, and is evaluated in an experiment on test objects, applying an industrial robot and a laser range scanner.