An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather (2504.14806v2)

Abstract: LiDAR place recognition (LPR) plays a vital role in autonomous navigation. However, existing LPR methods struggle to maintain robustness under adverse weather conditions such as rain, snow, and fog, where weather-induced noise and point cloud degradation impair LiDAR reliability and perception accuracy. To tackle these challenges, we propose an Iterative Task-Driven Framework (ITDNet), which integrates a LiDAR Data Restoration (LDR) module and a LiDAR Place Recognition (LPR) module through an iterative learning strategy. These modules are jointly trained end-to-end, with alternating optimization to enhance performance. The core rationale of ITDNet is to leverage the LDR module to recover the corrupted point clouds while preserving structural consistency with clean data, thereby improving LPR accuracy in adverse weather. Simultaneously, the LPR task provides feature pseudo-labels to guide the LDR module's training, aligning it more effectively with the LPR task. To achieve this, we first design a task-driven LPR loss and a reconstruction loss to jointly supervise the optimization of the LDR module. Furthermore, for the LDR module, we propose a Dual-Domain Mixer (DDM) block for frequency-spatial feature fusion and a Semantic-Aware Generator (SAG) block for semantic-guided restoration. In addition, for the LPR module, we introduce a Multi-Frequency Transformer (MFT) block and a Wavelet Pyramid NetVLAD (WPN) block to aggregate multi-scale, robust global descriptors. Finally, extensive experiments on Weather-KITTI, Boreas, and our proposed Weather-Apollo datasets demonstrate that, ITDNet outperforms existing LPR methods, achieving state-of-the-art performance in adverse weather.

An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather

The academic paper presents a sophisticated approach, referred to as the Iterative Task-Driven Framework (ITDNet), which aims to enhance the robustness of LiDAR Place Recognition (LPR) in adverse weather conditions. The motivation behind this research stems from the challenges posed by inclement weather such as rain, fog, and snow, which contribute to severe noise and degradation of LiDAR point cloud data, ultimately impairing the reliability and accuracy of place recognition systems integral to autonomous navigation technologies.

Proposed Framework: ITDNet

ITDNet comprises two synergistically integrated modules: the LiDAR Data Restoration (LDR) module and the LiDAR Place Recognition (LPR) module. The framework employs an innovative iterative learning strategy that alternates the training sequence between these modules to jointly optimize their performance.

1. LiDAR Data Restoration Module:
   • The LDR module is tasked with recovering corrupted point clouds while retaining structural integrity akin to clean data. It is built upon advanced components such as the Dual-Domain Mixer (DDM) block, facilitating frequency-spatial feature fusion, and the Semantic-Aware Generator (SAG) block, which incorporates semantic-guided restoration.
   • Training of the LDR module is governed by a dual loss mechanism—task-driven LPR loss and reconstruction loss—to ensure alignment with the objectives of enhancing LPR accuracy.
2. LiDAR Place Recognition Module:
   • The LPR module is designed to aggregate robust global descriptors through the architecture of a Multi-Frequency Transformer (MFT) block and a Wavelet Pyramid NetVLAD (WPN) block, thereby enabling refined feature extraction from multi-scale frequency domain representations.
   • The iterative strategy uses features from the LPR module as pseudo-labels to guide LDR module training, thus promoting improved synergy between restoration and recognition tasks.

Experimental Validation

The proposed ITDNet was rigorously evaluated using the Weather-KITTI, Boreas, and Weather-Apollo datasets, representing diverse adverse weather scenarios. The empirical results underscore the efficacy of ITDNet, demonstrating superior performance compared to current LPR methodologies, by outperforming state-of-the-art models across key metrics including Recall@N and perceptual similarity indices such as SSIM.

Implications and Future Directions

The implications of this framework are profound, notably in the domain of autonomous navigation systems. ITDNet holds promise for deployment in real-world applications, particularly enhancing vehicle navigation resilience in unpredictable and challenging weather environments. From a theoretical standpoint, the iterative learning strategy herein exemplified could be expanded beyond LiDAR-based systems to other domains within AI, where task-driven synergies between restoration and recognition might yield higher performance dividends.

Potential future extensions of this research might include exploring unsupervised solutions for LiDAR perception, directly addressing the limitation of data availability in adverse weather conditions and refining the ITDNet architecture for even broader applicability in AI-driven perception systems.

In summary, the development of ITDNet constitutes a meaningful advancement in enhancing LiDAR-based place recognition capabilities under adverse weather conditions, achieved through the novel integration of restoration and perception tasks driven by a sophisticated learning framework.