Knowledge Commons of Institute of Automation,CAS
Visual-Inertial Odometry Tightly Coupled with Wheel Encoder Adopting Robust Initialization and Online Extrinsic Calibration | |
Liu, Jinxu1,2![]() ![]() ![]() | |
2019-11-04 | |
会议名称 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
会议日期 | 2019年11月4日至8日 |
会议地点 | 中国澳门 |
出版者 | IEEE |
摘要 | Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4-degrees of freedom(DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refifine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally effificient online extrinsic calibration method by fifixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Experimental results prove the robustness of our initialization module and the accuracy of the whole trajectory, as well as the improvement brought about by online extrinsic calibration. Our program can also run on an Nvidia Jetson TX2 module in real time. |
七大方向——子方向分类 | 三维视觉 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44971 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 中国科学院自动化研究所 |
通讯作者 | Gao, Wei |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Jinxu,Gao, Wei,Hu, Zhanyi. Visual-Inertial Odometry Tightly Coupled with Wheel Encoder Adopting Robust Initialization and Online Extrinsic Calibration[C]:IEEE,2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
IROS.pdf(4876KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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