SR-LIO++: 基于缓存感知扫描重建的LiDAR-惯性里程计与量化建图
SR-LIO++: LiDAR-Inertial Odometry and Quantized Mapping with Caching-Aware Sweep Reconstruction
AI总结：
提出SR-LIO++系统，通过扫描重建提高输出频率，并采用缓存机制和量化地图点管理，在资源受限平台上实现高精度、高效率的LiDAR-惯性里程计。
摘要：
解决3D LiDAR固有低采集频率限制以实现高频输出已成为LiDAR-惯性里程计（LIO）领域的关键研究焦点。为确保实时性能，频率增强的LIO系统必须在显著缩短的时间窗口内处理每次扫描，这对在资源受限平台上的部署提出了巨大挑战。为解决这些限制，我们引入了SR-LIO++，一种创新的LIO系统，能够在资源受限的硬件平台（包括Raspberry Pi 4B）上实现相对于输入频率两倍的输出频率。我们的系统采用先前提出的扫描重建方法来提高LiDAR扫描频率，生成高频重建扫描。在此基础之上，我们提出了一种针对最近段中间结果（即表面参数）的缓存机制，有效减少了相邻重建扫描中公共段的冗余处理。该方法将处理时间从传统的对重建扫描频率的线性依赖中解耦出来。此外，我们提出了一种基于索引表映射的量化地图点管理，通过将全局3D点存储从64位双精度转换为8位字符表示，显著减少了内存使用。该方法还将最近邻搜索中计算密集的欧几里得距离计算从64位双精度转换为16位短整型和32位整型格式，降低了计算成本。在三个不同计算平台和四个公开数据集上的广泛实验评估表明，SR-LIO++在保持最先进精度的同时，显著提高了效率。值得注意的是，我们的系统在Raspberry Pi 4B硬件上成功实现了20 Hz的状态输出。
英文摘要：
Addressing the inherent low acquisition frequency limitation of 3D LiDAR to achieve high-frequency output has become a critical research focus in the LiDAR-Inertial Odometry (LIO) domain. To ensure real-time performance, frequency-enhanced LIO systems must process each sweep within significantly reduced timeframe, which presents substantial challenges for deployment on resource-constrained platforms. To address these limitations, we introduce SR-LIO++, an innovative LIO system capable of achieving doubled output frequency relative to input frequency on resource-constrained hardware platforms, including the Raspberry Pi 4B. Our system employs the previously proposed sweep reconstruction methodology to enhance LiDAR sweep frequency, generating high-frequency reconstructed sweeps. Building upon this foundation, we propose a caching mechanism for intermediate results (i.e., surface parameters) of the most recent segments, effectively minimizing redundant processing of common segments in adjacent reconstructed sweeps. This method decouples processing time from the traditionally linear dependence on reconstructed sweep frequency. Furthermore, we present a quantized map point management based on index table mapping, significantly reducing memory usage by converting global 3D point storage from 64-bit double precision to 8-bit char representation. This method also converts the computationally intensive Euclidean distance calculations in nearest neighbor searches from 64-bit double precision to 16-bit short and 32-bit integer formats, reducing computational cost. Extensive experimental evaluations across three distinct computing platforms and four public datasets demonstrate that SR-LIO++ maintains state-of-the-art accuracy while substantially enhancing efficiency. Notably, our system successfully achieves 20 Hz state output on Raspberry Pi 4B hardware.
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