A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments
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Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it