Bayesian iterative closest point for mobile robot localization
Bayesian iterative closest point for mobile robot localization
复制标题
DOI：
10.1177/02783649221101417
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
F. A. Maken;Fabio Ramos;Lionel Ott
中科院分区：
文献类型：
--
作者：
F. A. Maken;Fabio Ramos;Lionel Ott
机器人在已知环境中的精确定位是成功执行路径规划、操作和抓取任务的基本能力。粒子滤波，也被称为蒙特卡罗定位（MCL），是一种常用的方法来确定机器人在其环境中的姿态。对于地面机器人来说，噪声车轮里程计读数通常被用作预测车辆位置的运动模型。这种运动模型需要根据地形和机器人类型调整各种参数。然而，这种自我运动估计并不总是适用于所有平台。使用迭代最近点（ICP）算法的扫描匹配是一种流行的替代方法，为定位提供自我运动估计。迭代最近点计算在这些位置捕获的给定点云的两个姿态之间变换的点估计。ICP是一种点估计方法，没有处理扫描对准过程中的不确定性，这些不确定性可能是由传感器噪声、部分重叠或多个解的存在引起的。ICP的另一个挑战是对齐两个大型点云所需的高计算成本，这限制了它在不太动态的问题上的适用性。在本文中，我们通过利用概率推理的最新进展来解决这些挑战。具体而言，我们首先解决了运行时问题，并提出了SGD-ICP，该算法采用随机梯度下降（SGD）来解决ICP的优化问题。接下来，我们利用SGD-ICP来获得变换上的分布，并提出了使用随机梯度朗格万动力学（SGLD）更新的马尔可夫链蒙特卡罗方法。我们的ICP变体，称为Bayesian-ICP，是该问题的完全贝叶斯解决方案。为了证明贝叶斯icp在移动机器人应用中的优势，我们提出了一个自适应运动模型，利用贝叶斯icp来产生蒙特卡洛定位的建议分布。使用Kinect和3D LiDAR数据进行的实验表明，我们提出的SGD-ICP方法可以获得与标准ICP相同的解决方案质量，同时显着提高效率。然后，我们通过经验证明贝叶斯icp可以在姿态变换上产生准确的分布，并且对于在线应用程序来说足够快。最后，使用贝叶斯icp作为运动模型减轻了从里程计调整运动模型参数的需要，从而获得更好的校准定位不确定性。
Accurate localization of a robot in a known environment is a fundamental capability for successfully performing path planning, manipulation, and grasping tasks. Particle filters, also known as Monte Carlo localization (MCL), are a commonly used method to determine the robot’s pose within its environment. For ground robots, noisy wheel odometry readings are typically used as a motion model to predict the vehicle’s location. Such a motion model requires tuning of various parameters based on terrain and robot type. However, such an ego-motion estimation is not always available for all platforms. Scan matching using the iterative closest point (ICP) algorithm is a popular alternative approach, providing ego-motion estimates for localization. Iterative closest point computes a point estimate of the transformation between two poses given point clouds captured at these locations. Being a point estimate method, ICP does not deal with the uncertainties in the scan alignment process, which may arise due to sensor noise, partial overlap, or the existence of multiple solutions. Another challenge for ICP is the high computational cost required to align two large point clouds, limiting its applicability to less dynamic problems. In this paper, we address these challenges by leveraging recent advances in probabilistic inference. Specifically, we first address the run-time issue and propose SGD-ICP, which employs stochastic gradient descent (SGD) to solve the optimization problem of ICP. Next, we leverage SGD-ICP to obtain a distribution over transformations and propose a Markov Chain Monte Carlo method using stochastic gradient Langevin dynamics (SGLD) updates. Our ICP variant, termed Bayesian-ICP, is a full Bayesian solution to the problem. To demonstrate the benefits of Bayesian-ICP for mobile robotic applications, we propose an adaptive motion model employing Bayesian-ICP to produce proposal distributions for Monte Carlo Localization. Experiments using both Kinect and 3D LiDAR data show that our proposed SGD-ICP method achieves the same solution quality as standard ICP while being significantly more efficient. We then demonstrate empirically that Bayesian-ICP can produce accurate distributions over pose transformations and is fast enough for online applications. Finally, using Bayesian-ICP as a motion model alleviates the need to tune the motion model parameters from odometry, resulting in better-calibrated localization uncertainty.