Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization
Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization
复制标题
DOI：
10.1109/lra.2018.2849506
复制
发表时间：
2018-10-01
影响因子：
5.2
通讯作者：
Hutter, Marco
中科院分区：
文献类型：
--
作者：
Fankhauser, Peter;Bloesch, Michael;Hutter, Marco
移动的机器人建立在精确的，实时的地图与机载范围传感器，以实现自主导航崎岖的地形。现有的方法通常依赖于基于外部几何或视觉特征跟踪的绝对定位。为了规避这些方法的可靠性问题，我们提出了一种新的地形映射方法，它的基础上本体定位运动学和惯性测量。所提出的方法结合了漂移和不确定性的状态估计和噪声模型的距离传感器。它产生一个概率地形估计作为一个基于网格的海拔地图，包括上，下置信界。我们证明了我们的方法的有效性与模拟数据集和真实世界的实验，实时地形映射与腿机器人和地面真实参考地图的地形重建比较。
Mobile robots build on accurate, real-time mapping with onboard range sensors to achieve autonomous navigation over rough terrain. Existing approaches often rely on absolute localization based on tracking of external geometric or visual features. To circumvent the reliability issues of these approaches, we propose a novel terrain mapping method, which bases on proprioceptive localization from kinematic and inertial measurements only. The proposed method incorporates the drift and uncertainties of the state estimation and a noise model of the distance sensor. It yields a probabilistic terrain estimate as a grid-based elevation map including upper and lower confidence bounds. We demonstrate the effectiveness of our approach with simulated datasets and real-world experiments for real-time terrain mapping with legged robots and compare the terrain reconstruction to ground truth reference maps.