Abstract:The paper describes the case study of the mobile robot Andabata navigating on natural terrain at low speeds with fuzzy elevation maps (FEMs). To this end, leveled three-dimensional (3D) point clouds of the surroundings are obtained by synchronizing ranges obtained from a 360 • field of view 3D laser scanner with odometric and inertial measurements of the vehicle. Then, filtered point clouds are employed to produce FEMs and their corresponding fuzzy reliability masks (FRMs). Finally, each local FEM and its FRM are processed to choose the best motion direction to reach distant goal points through traversable areas. All these tasks have been implemented using ROS (Robot Operating System) nodes distributed among the cores of the onboard processor. Experimental results of Andabata during non-stop navigation on an urban park are presented.
Many applications, like mobile robotics, can profit from acquiring dense, wide-ranging and accurate 3D laser data. Off-the-shelf 2D scanners are commonly customized with an extra rotation as a low-cost, lightweight and low-power-demanding solution. Moreover, aligning the extra rotation axis with the optical center allows the 3D device to maintain the same minimum range as the 2D scanner and avoids offsets in computing Cartesian coordinates. The paper proposes a practical procedure to estimate construction misalignments based on a single scan taken from an arbitrary position in an unprepared environment that contains planar surfaces of unknown dimensions. Inherited measurement limitations from low-cost 2D devices prevent the estimation of very small translation misalignments, so the calibration problem reduces to obtaining boresight parameters. The distinctive approach with respect to previous plane-based intrinsic calibration techniques is the iterative maximization of both the flatness and the area of visible planes. Calibration results are presented for a case study. The method is currently being applied as the final stage in the production of a commercial 3D rangefinder.
Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describes the case study of mobile robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using the robot operating system (ROS) were performed on natural environments at low speeds.
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