CI-VO results for sequence 10 in the KITTI dataset, likely containing performance metrics for a visual odometry algorithm. The data was contributed by Min Yue from Zhejiang University and is hosted on the Papers with Code platform. The specific metrics, data volume, and update recency are not detailed in the available metadata.
Use Cases
- Benchmark visual odometry algorithm performance against a known sequence (inferred from domain, verify after download)
- Analyze trajectory estimation error in an autonomous driving context (inferred from domain, verify after download)
- Validate sensor fusion or localization methods using KITTI data (inferred from domain, verify after download)
Strengths
- Published on the Papers with Code platform, which is associated with machine learning research.
- Author affiliation is provided (Zhejiang University).
- License is specified as Open Access (green).
Limitations
- Metadata is minimal; actual content requires verification after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count, file size, and last update date are unknown, which may limit suitability assessment.
Provenance
- Source
- Min Yue, Zhejiang University
- Collection Method
- Likely results from running a CI-VO algorithm on the KITTI dataset sequence 10.