Laser-visual-inertial odometry and mapping with high robustness and low drift
Laser-visual-inertial odometry and mapping with high robustness and low drift
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DOI：
10.1002/rob.21809
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发表时间：
2018-12-01
影响因子：
8.3
通讯作者：
Singh, Sanjiv
中科院分区：
文献类型：
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作者：
Zhang, Ji;Singh, Sanjiv
我们提出了一个数据处理管道来在线估计自我运动并构建遍历环境的地图，利用来自3D激光扫描仪，相机和惯性测量单元（IMU）的数据。与使用卡尔曼滤波器或因子图优化的传统方法不同，所提出的方法采用顺序的多层处理流水线，从粗到细求解运动。从用于运动预测的IMU机械化开始，视觉-惯性耦合方法估计运动;然后，扫描匹配方法进一步细化运动估计并配准地图。由此产生的系统能够实现高频、低延迟的自我运动估计，沿着密集、准确的3D标测图配准。此外，该方法能够通过绕过故障模块的自动重新配置来处理传感器降级。因此，它可以在高度动态运动的情况下以及在黑暗、无纹理和无结构的环境中工作。在实验中，该方法证明了0.22%的相对位置漂移超过9.3公里的导航和鲁棒性w.r.t.跑步、跳跃，甚至高速公路高速驾驶（最高33米/秒）。
We present a data processing pipeline to online estimate ego-motion and build a map of the traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). Different from traditional methods that use a Kalman filter or factor-graph optimization, the proposed method employs a sequential, multilayer processing pipeline, solving for motion from coarse to fine. Starting with IMU mechanization for motion prediction, a visual-inertial coupled method estimates motion; then, a scan matching method further refines the motion estimates and registers maps. The resulting system enables high-frequency, low-latency ego-motion estimation, along with dense, accurate 3D map registration. Further, the method is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in the dark, texture-less, and structure-less environments. During experiments, the method demonstrates 0.22% of relative position drift over 9.3 km of navigation and robustness w.r.t. running, jumping, and even highway speed driving (up to 33 m/s).