论文标题
通过在线外部校准的多大磁盘系统的强大探子仪和映射
Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration
论文作者
论文摘要
组合多个痛使机器人能够最大程度地提高其对环境的感知意识并获得足够的测量，这对于同时定位和映射（SLAM）是有希望的。本文提出了一个系统，以实现多个激光痛的鲁棒和同时进行外部校准，探测和映射。我们的方法始于测量预处理，以从原始测量中提取边缘和平面特征。在运动和外部初始化过程之后，基于滑动窗口的多尺寸多尺寸探测器在船上运行以估算在线校准改进和收敛识别的姿势。我们进一步开发了一种映射算法来构建全局地图，并具有足够功能的姿势以及一种模型和减少数据不确定性的方法。我们通过对校准的十个序列（总长度为4.60 km）进行广泛的实验来验证方法的性能，然后将它们与最新的校准进行比较。我们证明了所提出的工作是针对各种多LILDAR设置的完整，健壮且可扩展的系统。源代码，数据集和演示可在https://ram-lab.com/file/site/m-loam上找到。
Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to model and reduce data uncertainty. We validate our approach's performance with extensive experiments on ten sequences (4.60km total length) for the calibration and SLAM and compare them against the state-of-the-art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at https://ram-lab.com/file/site/m-loam.
