A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
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发表时间：
2019-01
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通讯作者：
Tong Qin;Jie Pan;Shaozu Cao;S. Shen
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作者：
Tong Qin;Jie Pan;Shaozu Cao;S. Shen
精确的状态估计是自主机器人的一个基本问题。为了实现局部精确且全局无漂移的状态估计，通常将具有互补特性的多个传感器融合在一起。局部传感器（相机、惯性测量单元、激光雷达等）在小区域内提供精确的位姿，而全局传感器（全球定位系统、磁力计、气压计等）在大规模环境中提供有噪声但全局无漂移的定位。在本文中，我们提出了一个传感器融合框架，将局部状态与全局传感器相融合，实现了局部精确且全局无漂移的位姿估计。由现有的视觉里程计/视觉惯性里程计方法产生的局部估计，在一个位姿图优化中与全局传感器相融合。在位姿图优化中，局部估计被对齐到一个全局坐标系中。同时，累积的漂移被消除。我们在公开数据集上以及通过实际实验评估了我们系统的性能。将结果与其他最先进的算法进行了比较。我们强调我们的系统是一个通用框架，它可以在统一的位姿图优化中轻松融合各种全局传感器。我们的实现是开源的\(^{\text{注：this https URL}}\)
Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnote{this https URL}.