基于视觉-IMU-编码器的SLAM新算法

VINS-Mono算法应用于轮式机器人时，由于惯性测量单元(inertial measurement unit, IMU)加速度计信噪比较小，观测尺度不准确，会出现定位精度下降。对此，提出了一种融合单目相机、惯性测量单元和编码器的改进算法。在VINS-Mono初始化和后端优化的目标函数中，增加编码器测量残差项，直接融合由编码器数据计算的速度，增强尺度的可观性，降低定位累积误差，提高了定位精度。另外，针对车轮打滑造成编码器速度测量不准的问题，利用IMU角速度计测量值计算打滑因子，自适应调整编码器残差项在目标函数中的权重及其鲁棒核函数的阈值，减小车轮打滑对定位结果的影响。在两轮移动机器人上的实验表明，改进算法具有较强的鲁棒性，定位精度比VINS-Mono提高了一个数量级。

A SLAM Algorithm Based on Vision-IMU-Encoder

When the VINS-Mono is applied to a wheeled robot, due to the low signal-to-noise ratio of the inertial measurement unit(IMU) accelerometer and the inaccurate observation scale, the localization accuracy will decrease. For this problem, an improved algorithm that fusion of monocular camera, IMU and encoder is proposed. The encoder measurement residual term is added to the objective function of VINS-Mono initialization and back-end optimization. The speed calculated by the encoder data is directly fused to enhance the observability of the scale, reduce the accumulated localization error, and improve the localization accuracy. Moreover, in order to reduce the influence of wheel slip on the localization accuracy, the slip factor is calculated by IMU gyro data to adaptively adjust the weight of the encoder measurement residual in the objective function and the threshold of its robust kernel function. Experiments on a two-wheel robot show that the improved algorithm has high robustness, and the localization accuracy of the improved algorithm is an order of magnitude higher than VINS-Mono.