6D SLAM (simultaneous localization and mapping) or 6D concurrent localization and mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y, and z coordinates and the roll, yaw, and pitch angles. Robot motion and localization on natural surfaces, e.g., driving outdoor with a mobile robot, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system. A new strategy for fast data association, cached kd-tree search, leads to feasible computing times. With no ground-truth data available for outdoor environments, point relations in maps are compared to numerical relations in uncalibrated aerial images in order to assess the metric validity of the resulting 3D maps.
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