QLIO通过分处理器量化处理，让车载导航更省电高效
QLIO: Quantized LiDAR-Inertial Odometry
- 用协处理器预处理激光数据，压缩残差后只传关键特征
- 实测每帧数据量减少14.1%，定位精度基本不变
- 适合嵌入式设备，开源代码助力部署与研究
激光惯性里程计（LIO）广泛用于自动驾驶导航，但在尺寸、重量和功耗（SWaP）受限的平台部署仍具挑战，主要因处理密集点云计算成本高。传统LIO框架依赖单一主处理器，导致计算瓶颈和高内存需求，难以在嵌入式系统实现实时运行。为此，我们提出QLIO——一种多处理器分布式量化激光惯性里程计框架，通过降低计算负载和带宽消耗，同时保持定位精度。QLIO引入量化状态估计流程，协处理器预先处理激光测量，压缩点面残差后仅传输关键特征至主处理器；此外，采用rQ向量自适应重采样策略，智能选择并压缩关键观测，进一步减少计算冗余。真实世界测试表明，QLIO实现每观测残差数据量减少14.1%的同时，仍保持定位精度。我们还开源了实现代码，以促进后续研究与实际部署。结果表明，QLIO是面向计算与带宽受限场景的实时自主系统的一种高效且可扩展解决方案。
原文摘要 · Abstract (English)
LiDAR-Inertial Odometry (LIO) is widely used for autonomous navigation, but its deployment on Size, Weight, and Power (SWaP)-constrained platforms remains challenging due to the computational cost of processing dense point clouds. Conventional LIO frameworks rely on a single onboard processor, leading to computational bottlenecks and high memory demands, making real-time execution difficult on embedded systems. To address this, we propose QLIO, a multi-processor distributed quantized LIO framework that reduces computational load and bandwidth consumption while maintaining localization accuracy. QLIO introduces a quantized state estimation pipeline, where a co-processor pre-processes LiDAR measurements, compressing point-to-plane residuals before transmitting only essential features to the host processor. Additionally, an rQ-vector-based adaptive resampling strategy intelligently selects and compresses key observations, further reducing computational redundancy. Real-world evaluations demonstrate that QLIO achieves a 14.1% reduction in per-observation residual data while preserving localization accuracy. Furthermore, we release an open-source implementation to facilitate further research and real-world deployment. These results establish QLIO as an efficient and scalable solution for real-time autonomous systems operating under computational and bandwidth constraints.
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