Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible to failure when a robot sustains extreme velocities and accelerations. For example, this kind of motion can happen after a collision, causing a point cloud to be heavily skewed. While point cloud de-skewing methods have been explored in the past to increase localization and mapping accuracy, these methods still rely on highly accurate odometry systems or ideal navigation conditions. In this paper, we present a method taking into account the remaining motion uncertainties of the trajectory used to de-skew a point cloud along with the environment geometry to increase the robustness of current registration algorithms. We compare our method to three other solutions in a test bench producing 3D maps with peak accelerations of 200 m/s^2 and 800 rad/s^2. In these extreme scenarios, we demonstrate that our method decreases the error by 9.26 % in translation and by 21.84 % in rotation. The proposed method is generic enough to be integrated to many variants of weighted ICP without adaptation and supports localization robustness in harsher terrains.
翻译:过去几十年来,热极近点(IPC)等登记算法在移动机器人本地化算法中证明是有效的。 但是,当机器人维持极端速度和加速度时,它们很容易发生故障。例如,这种动作可能在碰撞后发生,导致点云严重偏斜。过去曾探索过点云脱偏斜方法以提高本地化和绘图精确度,但这些方法仍然依赖于高度精确的odomization系统或理想的导航条件。在本文中,我们提出了一个方法,其中考虑到用于除去点云的轨迹以及环境几何方法的剩余运动不确定性,以提高当前登记算法的稳健性。我们将我们的方法与其他三种解决办法进行比较,在产生3D地图的试验台站台中,以200 m/s%2 和800 rad/s%2 的最高加速度。在这些极端情况下,我们证明我们的方法在翻译中减少了9.26%的误差,在轮换中减少了21.84%的误差。拟议方法非常通用,足以与许多加权的比较性比较比较比较比较比较方案,不作调整后支持严格的地形。