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 based on sliding window 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 refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Fourthly, we give an observability analysis of our optimization based approach under more general assumption. Extensive experiments are performed on two sets of data in various scenes, bringing the state-of-the-art visual odometry and visual-inertial odometry algorithms into comparison. Experimental results prove the overwhelmingly better performance of our proposed approach on the above two sets of data, as well as the robustness of our initialization module and the improvement resulted from online extrinsic calibration.

中文翻译：

---

具有鲁棒初始化和在线外参标定的视觉惯性轮里程计的实现和可观测性分析

将摄像头、IMU 和车轮编码器结合起来是汽车定位的明智选择，因为传感器成本低廉且具有互补性。我们提出了一种基于滑动窗口紧密融合来自上述三个传感器的数据的新型扩展视觉惯性里程计算法。首先，我们提出了一种利用完整 IMU 测量和车轮编码器读数的 IMU-里程计预积分方法，以使后续 4 自由度 (DoF) 优化中的尺度估计更加准确。其次，我们开发了一个原始的初始化模块，其中编码器读数被充分利用来细化重力方向并为真实比例的相机位姿提供初始值。第三，我们通过固定 IMU 里程表外在参数的旋转分量的线性化点，设计了一种计算高效的在线外在校准方法，该方法根据加速度计偏差的收敛进行部署。第四，我们在更一般的假设下对基于优化的方法进行了可观测性分析。对不同场景下的两组数据进行了广泛的实验，将最先进的视觉里程计和视觉惯性里程计算法进行了比较。实验结果证明我们提出的方法在上述两组数据上具有压倒性的更好的性能，以及我们的初始化模块的鲁棒性和在线外在校准带来的改进。