リソース制約環境向け軽量LiDAR-慣性-視覚オドメトリ
FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation
リソース制約のあるプラットフォーム向けに、適応的視覚フレーム選択と省メモリ地図構造を組み込んだ軽量LiDAR-慣性-視覚オドメトリを提案し、精度をほぼ維持しつつ計算量とメモリを削減した。
著者: Bingyang Zhou, Chunran Zheng, Ziming Wang, Fangcheng Zhu, Yixi Cai, Fu Zhang
分類: cs.RO
原文アブストラクト
This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency significantly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system's robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system's capability for scalable deployment on resource-constrained edge computing platforms.
