轻量化激光惯性视觉里程计，高效运行于低资源设备
FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation
- 自适应视觉帧选择融合误差状态卡尔曼滤波，提升计算效率
- 每帧耗时降33%，内存减少47%，仅增3厘米定位误差
- 适合边缘设备部署，对实时性与内存敏感的应用场景
本文提出一种专为资源受限平台优化的轻量级激光惯性视觉里程计系统。通过在误差状态迭代卡尔曼滤波（ESIKF）中引入退化感知的自适应视觉帧选择机制，并采用顺序更新策略，显著提升计算效率，同时保持相近的鲁棒性。此外，设计了一种结合局部统一视觉-激光地图与长期视觉地图的记忆高效映射结构，在性能与内存占用间取得良好平衡。在x86和ARM平台上进行的大量实验表明，该系统具有优异的鲁棒性和效率。在Hilti数据集上，相较FAST-LIVO2，本系统实现每帧运行时间降低33%、内存使用减少47%，仅增加3厘米的均方根误差（RMSE）。尽管存在轻微精度损失，系统仍优于现有先进激光惯性里程计（LIO）方法如FAST-LIO2及多数主流激光惯性视觉（LIVO）系统。结果验证了其在资源受限边缘计算平台上的可扩展部署能力。
原文摘要 · Abstract (English)
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.
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