IMU-Preintegrated Radar Factors for Asynchronous Radar-LiDAR-Inertial SLAM

2025-09-26

AI Summary

In radar–LiDAR–inertial SLAM, asynchronous sensor operation causes excessive state node proliferation and prohibitive optimization overhead. To address this, we propose an IMU preintegration-based radar factor modeling method: IMU preintegration is employed to propagate LiDAR states to radar measurement timestamps, eliminating the need for dedicated state nodes per radar frame and reducing state variable generation frequency by 50%. This work is the first to incorporate IMU preintegration into radar factor design, enabling tightly coupled asynchronous fusion while preserving the original LiDAR node frequency—thereby significantly compressing the factor graph size. Experiments on an embedded single-board computer demonstrate a 56% reduction in overall optimization time, with absolute pose accuracy matching that of conventional synchronous approaches. The method thus achieves an effective trade-off between real-time performance and localization accuracy.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Fixed-lag Radar-LiDAR-Inertial smoothers conventionally create one factor graph node per measurement to compensate for the lack of time synchronization between radar and LiDAR. For a radar-LiDAR sensor pair with equal rates, this strategy results in a state creation rate of twice the individual sensor frequencies. This doubling of the number of states per second yields high optimization costs, inhibiting real-time performance on resource-constrained hardware. We introduce IMU-preintegrated radar factors that use high-rate inertial data to propagate the most recent LiDAR state to the radar measurement timestamp. This strategy maintains the node creation rate at the LiDAR measurement frequency. Assuming equal sensor rates, this lowers the number of nodes by 50 % and consequently the computational costs. Experiments on a single board computer (which has 4 cores each of 2.2 GHz A73 and 2 GHz A53 with 8 GB RAM) show that our method preserves the absolute pose error of a conventional baseline while simultaneously lowering the aggregated factor graph optimization time by up to 56 %.

Problem

• Reduces computational costs in radar-LiDAR-inertial SLAM systems
• Maintains accuracy while lowering optimization time by up to 56%
• Solves asynchronous sensor data synchronization using IMU preintegration

Innovation

• IMU-preintegrated radar factors for asynchronous sensor fusion
• Propagates LiDAR state to radar timestamp using IMU
• Reduces factor graph nodes by 50% maintaining accuracy