Tight Integration of INS/Stereo VO/Digital Map for Land Vehicle Navigation

Abstract

This paper proposes a method for tight integration of IMU (Inertial Measurement Unit), stereo <small>VO</small> (Visual Odometry) and digital map for land vehicle navigation, which effectively limits the quick drift of <small>DR</small> (Dead Reckoning) navigation system. In this method, the <small>INS</small> provides the dynamic information of the land vehicle, which is used to predict the position and attitude of cameras in order to obtain the predicted pixel coordinates of features on the image. The difference between the measured and predicted pixel coordinates is used to reduce the accumulated errors of <small>INS</small>. To implement the proposed method, an Extended Kalman filter (<small>EKF</small>) is first used to integrate the inertial and visual sensor data. The integrated solution of position, velocity and azimuth is then applied by fuzzy logic map matching (<small>MM</small>) to project the vehicle location on the correct road link. The projected position on the road link and the road link azimuth can finally be used to reduce the dead reckoning drifts. In this way, the accumulated system errors can be significantly reduced. The testing results indicate that the horizontal <small>RMSE</small> (root-mean-square-error) of the proposed method is less than 20 meters over a traveled distance of five kilometers and the relative horizontal error is below 0.4 percent.

Stereo visual odometry with velocity constraint for ground vehicle applications

“…However, the performance of GNSS degrades in challenging environments such as dense urban areas, tunnels, or underground parking lots due to GNSS signal blockage and reflection (Groves and Jiang, 2013). Dead reckoning (DR) systems such as inertial navigation system (INS) or stereo visual odometry (VO) are widely used as systems complementary to GNSS in GNSS-denied environments (Liu, 2018;F. Liu et al, 2018aF.…”

“…The inertial measurement unit (IMU) is widely used in multi-sensor integration applications as the core sensor since it works in most circumstances (Hartley and Zisserman, 2001;Balazadegan Sarvrood, 2016;F. Liu et al, 2018aF.…”

Improving robustness of the MAV yaw angle estimation for low‐cost INS/GPS integration aided with tri‐axial magnetometer calibrated by rotating the ellipsoid model

“…So, a great amount of research is done to improve the robustness and accuracy of INS/GPS based on low‐cost IMU, e.g. non‐linear filtering algorithms developed to compensate the non‐linearity error of integration navigation system [1, 2], vision‐aided navigation [3, 4], adaptively‐robust strategy to control the influences of model errors and measurement disturbance [5–7], and map‐aided positioning [8, 9]. Based on the assumption that the vehicle is in variable motion, all attitude angles are observable to some extent aided by external information about yaw angle.…”