Camera-odometer calibration and fusion using graph based optimization
He YJ(贺一家)1,2; Guo Yue(郭跃)1,2; Ye Aixue(叶爱学)1,2; Yuan Kui(原魁)1,2
2018-03
会议名称2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)
会议日期2017-12
会议地点澳门
摘要Monocular visual odometry (vo) estimates the camera motion only up to a scale which is prone to localization failure when the light is changing. The wheel encoders can provide metric information and accurate local localization. Fusing camera information with wheel odometer data is a good way to estimate robot motion. In such methods, calibrating camera-odometer extrinsic parameters and fusing sensor information to perform localization are key problems. We solve these problems by transforming the wheel odometry measurement to the camera frame that can construct a factor-graph edge between every two keyframes. By building factor graph, we can use graph-based optimization technology to estimate camera odometer extrinsic parameters and fuse sensor information to estimate robot motion. We also derive the covariance matrix of the wheel odometry edges which is important when using graph-based optimization. Simulation experiments are used to validate the extrinsic calibration. For real-world experiments, we use our method to fuse the semi-direct visual odometry (SVO) with wheel encoder data, and the results show the fusion approach is effective.
关键词Sensor Fusion Localization
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/21066
专题智能制造技术与系统研究中心_智能机器人
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
He YJ,Guo Yue,Ye Aixue,et al. Camera-odometer calibration and fusion using graph based optimization[C],2018.
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