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QLIO: Quantized LiDAR-Inertial Odometry

11 March 2025
Boyang Lou
Shenghai Yuan
Jianfei Yang
Wenju Su
Yingjian Zhang
Enwen Hu
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Abstract

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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@article{lou2025_2503.07949,
  title={ QLIO: Quantized LiDAR-Inertial Odometry },
  author={ Boyang Lou and Shenghai Yuan and Jianfei Yang and Wenju Su and Yingjian Zhang and Enwen Hu },
  journal={arXiv preprint arXiv:2503.07949},
  year={ 2025 }
}
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