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We present an odometry‐free three‐dimensional (3D) point cloud registration strategy for outdoor environments based on area attributed planar patches. The approach is split into three steps. The first step is to segment each point cloud into planar segments, utilizing a cached‐octree region growing algorithm, which does not require the 2.5D image‐like structure of organized point clouds. The second step is to calculate the area of each segment based on small local faces inspired by the idea of surface integrals. The third step is to find segment correspondences between overlapping point clouds using a search algorithm, and compute the transformation from determined correspondences. The transformation is searched globally so as to maximize a spherical correlation‐like metric by enumerating solutions derived from potential segment correspondences. The novelty of this step is that only the area and plane parameters of each segment are employed, and no prior pose estimation from other sensors is required. Four datasets have been used to evaluate the proposed approach, three of which are publicly available and one that stems from our custom‐built platform. Based on these datasets, the following evaluations have been done: segmentation speed benchmarking, segment area calculation accuracy and speed benchmarking, processing data acquired by scanners with different fields of view, comparison with the iterative closest point algorithm, robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth. Experimental results confirm that the approach offers an alternative to state‐of‐the‐art algorithms in plane‐rich environments.

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This paper introduces a custom-built unmanned aerial vehicle, capable of autonomous exploration in urban environments. It consists of a multicopter, an inertial navigation system and two 2D laser range finders. In addition to a description of the hardware architecture and individual components being used, the authors also discuss challenges and problems that arose during its construction as well as optimizations and workarounds applied in the course of its development.Also presented is the software architecture, with a focus on a novel algorithm capable of generating multiple next best views (NBVs), sorted by achievable information gain. Although being designed for application on airborne platforms in urban environments, it works directly on raw point clouds and thus can be used with any sensor generating spatial occupancy information (e.g. LIDAR, RGBD-or time-of-flight-cameras). To satisfy constraints introduced by real-time operation on UAVs, the algorithm is implemented on a highly parallel SIMD architecture and benchmarked using GPUs from multiple hardware generations, using data from real flights. It is also compared against the previous, CPU-based proof of concept.As the underlying hardware imposes limitations with regards to memory access and concurrency, necessary data structures and further performance considerations are explained in detail.Open-source code for this paper is available at http://www.github.com/benadler/octocopter/.

Abstract-This work documents our progress on building an unmanned aerial vehicle capable of autonomously mapping urban environments. This includes localization and tracking of the vehicle's pose, fusion of sensor-data from onboard GNSS receivers, IMUs, laserscanners and cameras as well as realtime path-planning and collision-avoidance. Currently, we focus on a physics-based approach to computing waypoints, which are subsequently used to steer the platform in three-dimensional space. Generation of efficient sensor trajectories for maximized information gain operates directly on unorganized point clouds, creating a perfect fit for environment mapping with commonly used LIDAR sensors and time-of-flight cameras. We present the algorithm's application to real sensor-data and analyze its performance in a virtual outdoor scenario.