LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle Localization
Source abstract
Abstract
Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. SMLAD: Simultaneous matching, localization, and detection for intelligent vehicle from LiDAR map with semantic likelihood model10.1109/TVT.2023.3321079 ↗
2. Efficient LiDAR/inertial-based localization with prior map for autonomous robots10.1007/s11370-023-00490-6 ↗
3. LIV-GaussMap: LiDAR-inertial-visual fusion for real-time 3D radiance field map rendering10.1109/LRA.2024.3400149 ↗
4. Insufficient environmental information indoor localization of mecanum mobile platform using wheel-visual-inertial odometry10.1007/s12206-024-0836-z ↗
5. Online robust sliding-windowed lidar slam in natural environments
6. Fast LiDAR localization using multiresolution gaussian mixture maps
7. Robust intensity-based localization method for autonomous driving on snow-wet road surface10.1109/TII.2017.2713836 ↗
8. Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes
9. Map-based precision vehicle localization in urban environments
10. Shady dealings: Robust, long-term visual localisation using illumination invariance
11. Work smart, not hard: Recalling relevant experiences for vast-scale but time-constrained localisation
12. Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera
13. Appearance-based landmark selection for efficient long-term visual localization
14. Bench- marking 6DOF outdoor visual localization in changing conditions
15. LOAM: LiDAR odometry and mapping in real-time
16. Real-time loop closure in 2D LiDAR SLAM
17. Low-drift and real-time LiDAR odometry and mapping10.1007/s10514-016-9548-2 ↗
18. Probabilistic surfel fusion for dense LiDAR mapping
19. Efficient surfel-based SLAM using 3D laser range data in urban environments
20. Efficient continuous-time SLAM for 3D LiDAR-based online mapping
21. Elastic LiDAR fusion: Dense map-centric continuous-time SLAM
22. LeGO-LOAM: Lightweight and ground-optimized LiDAR odometry and mapping on variable terrain
23. IMLS-SLAM: Scan-to-model matching based on 3D data
24. Net: Deep real-time LiDAR odometry
25. Multi-modal scene matching location algorithm based on M2Det
26. C. Qin, H. Ye, C. E. Pranata, J. Han, and M. Liu, “LINS: A LiDAR-Inerital state estimator for robust and fast navigation,” 2019, arXiv:1907.02233.10.1109/ICRA40945.2020.9197567 ↗
27. GNSS/INS/LiDAR- SLAM integrated navigation system based on graph optimization10.3390/rs11091009 ↗
28. Orb-slam2: An open-source slam system formonocular, stereo, and rgb-d cameras10.1109/TRO.2017.2705103 ↗
29. ICE-BA: Incremental, consistent and efficient bundle adjustment for visual-inertial slam
30. INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm10.3390/s150923286 ↗
31. A precise and robust segmentation-based LiDAR localization system for automated urban driving
32. A LiDAR-INS-aided geometry-based cycle slip resolution for intelligent vehicle in urban environment with long-term satellite signal loss10.1007/s10291-023-01597-0 ↗
33. Real-time monocular SLAM: Why filter?
34. Vehicle localization based On IMU, OBD2, and GNSS sensor fusion using extended kalman filter10.14716/ijtech.v14i6.6649 ↗
35. W. Zhen, H. Yu, Y. Hu, and S. Scherer, “Unified representation of geometric primitives for graph-slam optimization using decomposed quadrics,” 2021, arXiv:2108.08957.10.1109/ICRA46639.2022.9812162 ↗
36. Superpoint: Self- supervised interest point detection and description
37. Su-perglue: Learning feature matching with graph neural networks
38. AWLC: Adaptive weighted loop closure for SLAM with multi-modal sensor fusion10.1142/S0218126624502335 ↗
39. GNSS/IMU/LiDAR fusion for vehicle localization in urban driving environments within a consensus framework10.1016/j.ymssp.2023.110862 ↗
40. Method for registration of 3-D shapes
41. Generalized-icp
42. Deep global registration
43. D3Feat: Joint learning of dense detection and description of 3D local features
44. PCAM: Product of cross- attention matrices for rigid registration of point clouds
45. S. Huang, Z. Gojcic, M. Usvyatsov, A. Wieser, and K. Schindler, “PREDATOR: Registration of 3D point clouds with low overlap-supplementary material,” 2021, arXiv:2011.13005.10.1109/CVPR46437.2021.00425 ↗
46. HRegNet: A hierarchical network for large-scale outdoor lidar point cloud registration