FAST-LIVO2在资源受限平台上的应用：具有高效内存与计算的LiDAR-惯性-视觉里程计
FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation
AI总结：
本文提出一种轻量级LiDAR-惯性-视觉里程计系统，通过退化感知自适应视觉帧选择器和内存高效建图结构，在资源受限平台上实现计算与内存的大幅降低，同时保持竞争力。
摘要：
本文提出了一种针对资源受限平台优化的轻量级LiDAR-惯性-视觉里程计系统。该系统将一种退化感知的自适应视觉帧选择器集成到具有顺序更新的误差状态迭代卡尔曼滤波器（ESIKF）中，显著提高了计算效率，同时保持了相似的鲁棒性。此外，一种结合局部统一视觉-LiDAR地图和长期视觉地图的内存高效建图结构，在性能与内存使用之间取得了良好的平衡。在x86和ARM平台上的大量实验证明了该系统的鲁棒性和效率。在Hilti数据集上，与FAST-LIVO2相比，我们的系统每帧运行时间减少了33%，内存使用降低了47%，而均方根误差（RMSE）仅增加了3厘米。尽管存在这种轻微的精度权衡，我们的系统仍具有竞争力，优于FAST-LIO2等最先进的（SOTA）LIO方法以及大多数现有的LIVO系统。这些结果验证了该系统在资源受限的边缘计算平台上可扩展部署的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
