SR-LIO++: Efficient LiDAR-Inertial Odometry and Quantized Mapping with Sweep Reconstruction

2025-03-29

AI Summary

To address the challenge of low frame-rate 3D LiDAR sensors limiting real-time, high-frequency LiDAR-IMU tightly-coupled localization on resource-constrained platforms (e.g., Raspberry Pi), this paper proposes a lightweight frame-rate enhancement framework. Our method introduces: (i) a novel sweep-reconstruction-based frame-doubling mechanism; (ii) a surface-parameter caching strategy that decouples reconstruction computation from output frequency; and (iii) an indexed, quantized point cloud management scheme enabling 8-bit point storage and efficient 16-/32-bit nearest-neighbor search. Evaluated on a Raspberry Pi 4B, the framework achieves 20 Hz high-accuracy state estimation while reducing memory footprint by 87.5% and significantly lowering computational overhead—without compromising localization accuracy, which remains at the state-of-the-art level.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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 low-computational-power 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 a 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, significantly reducing both memory and 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 20Hz state output on Raspberry Pi 4B hardware.

Problem

• Enhance LiDAR sweep frequency for high-frequency odometry output
• Reduce computational load on resource-constrained hardware platforms
• Minimize memory usage with quantized map point management

Innovation

• Sweep reconstruction enhances LiDAR frequency
• Caching mechanism minimizes redundant segment processing
• Quantized map reduces memory and computational cost