エッジ上でのRGB-D SLAM：Raspberry Pi 5におけるRTAB-MapとORB-SLAM3のベンチマークとプロファイリング
RGB-D SLAM on the Edge: Benchmarking and Profiling RTAB-Map and ORB-SLAM3 on the Raspberry Pi 5
Raspberry Pi 5上でRTAB-MapとORB-SLAM3を6つのTUM RGB-Dシーケンス・3解像度で実行し、追跡率・誤差・処理時間・メモリ・CPU・温度を計測して段階別にプロファイリングした。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Himanshu Patil, Jaideep Joshi, Rohit Saluja, Ganesh Ramakrishnan
分類: cs.RO
原文アブストラクト
Micro aerial vehicles that operate without GPS often run SLAM on ARM-based edge boards, yet there is little published data on how standard RGB-D SLAM systems behave on such hardware. We benchmark RTAB-Map and ORB-SLAM3 on the Raspberry Pi 5 using six TUM RGB-D sequences at three image resolutions, recording tracking coverage, trajectory error, time per frame, memory, CPU load and temperature for every run, and we profile both systems at the stage and function level. RTAB-Map was configured to process and keep every frame, so its back end runs on every frame rather than at its default rate of 1 Hz. On the sequences that every configuration tracked, its front end takes 38-74 ms per frame and its full pipeline 150-462 ms, compared with 13-45 ms and 26-62 ms for ORB-SLAM3. RTAB-Map's main cost is optimizing a pose graph that keeps growing, because its working memory is unbounded by default; ORB-SLAM3's local bundle adjustment costs about as much as its tracking but runs in a parallel thread. At quarter resolution, both systems lose tracking on most sequences, and runs that lose tracking finish sooner and report lower error on the poses they keep, so time and accuracy must be read alongside coverage. Timing is repeatable to within 1-5%, but trajectory error varies by up to 27% between identical runs, so the accuracy differences between the two systems are inconclusive. No thermal throttling occurred.