Keskeiset käsitteet
LiDAR-based place recognition systems are evaluated and deployed in dense forest environments, showcasing their performance and potential applications.
Tiivistelmä
The content delves into the evaluation and deployment of LiDAR-based place recognition systems in dense forest environments. It covers the analysis of four different LiDAR place recognition models, the methodology used for evaluation, and the results obtained from various operational modes such as online SLAM, offline multi-mission SLAM map merging, and relocalization into a prior map. The study highlights the challenges faced in natural environments like forests, the importance of robust place recognition systems, and the potential applications for forestry or biodiversity monitoring. Detailed experiments, findings, and insights are provided to showcase the effectiveness of LiDAR technology in dense forest settings.
Directory:
- Abstract
- Evaluation of LiDAR place recognition systems in urban vs. natural environments.
- Introduction
- Importance of place recognition for SLAM systems.
- Related Work
- Review of handcrafted and learning-based methods for place recognition.
- Method
- Three tasks: Online SLAM, Offline multi-mission SLAM, Relocalization.
- Experimental Evaluation
- Testing descriptors' performance, online place recognition, offline multi-mission SLAM results.
- Conclusion
- Summary of findings and future applications.
Tilastot
Achieved 80% correct loop closures candidates with baseline distances up to 5 m.
Achieved 60% correct loop closures candidates with baseline distances up to 10 m.
Lainaukset
"Logg3dNet consistently outperforms other models across different forests."
"Our experiments provide insights on LiDAR-based place recognition methods in dense forests."