论文标题
使用Lie代数改善迭代的最接近点算法
Improving the Iterative Closest Point Algorithm using Lie Algebra
论文作者
论文摘要
依赖登记点云的映射算法不可避免地会在本地化和构建地图中都遭受本地漂移的影响。需要准确地图（例如环境监视）的应用程序受益于减少这种漂移的其他传感器方式。在我们的工作中，我们根据迭代最接近的点（ICP）算法来针对映射器家族，该算法使用其他方向源，例如惯性测量单元（IMU）。我们引入了一个新的角度罚款术语，该刑法源自Lie代数。我们的公式避免需要调整任意参数。取而代之的是方向协方差，结果误差项拟合到ICP成本函数最小化问题中。在我们自己的现实数据和Kitti数据集上执行的实验表现出一致的行为，同时抑制了IMU测量的效果。我们进一步讨论了有希望的实验，这应该导致ICP成本函数最小化问题中所有错误项的最佳组合，从而使我们能够平稳地结合机器人传感器提供的几何和惯性信息。
Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that require accurate maps, such as environmental monitoring, benefit from additional sensor modalities that reduce such drift. In our work, we target the family of mappers based on the Iterative Closest Point (ICP) algorithm which use additional orientation sources such as the Inertial Measurement Unit (IMU). We introduce a new angular penalty term derived from Lie algebra. Our formulation avoids the need for tuning arbitrary parameters. Orientation covariance is used instead, and the resulting error term fits into the ICP cost function minimization problem. Experiments performed on our own real-world data and on the KITTI dataset show consistent behavior while suppressing the effect of outlying IMU measurements. We further discuss promising experiments, which should lead to optimal combination of all error terms in the ICP cost function minimization problem, allowing us to smoothly combine the geometric and inertial information provided by robot sensors.
