AI Article Synopsis
- The research focuses on creating an advanced navigation system that combines data from IMU, LiDAR, and a monocular camera to enhance positioning accuracy when GNSS signals are weak or unavailable.
- It utilizes an extended Kalman filter to effectively address issues related to scale ambiguity in monocular visual odometry by integrating depth measurements from LiDAR.
- Testing on the KITTI and Leddar PixSet datasets showed significant improvements, with the system achieving an average RMSE reduction of 80% in horizontal positioning and 92% in vertical positioning compared to existing methods.
Article Abstract
This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments. The system yielded an average reduction in the root-mean-square error (RMSE) of about 80% and 92% in the horizontal and upward directions, respectively. The proposed system was compared with an INS/monocular visual SLAM/LiDAR SLAM integration and to some state-of-the-art SLAM algorithms.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346648 | PMC |
| http://dx.doi.org/10.3390/s23136019 | DOI Listing |
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