PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion (2410.04939v1)

Abstract: Place recognition plays a crucial role in the fields of robotics and computer vision, finding applications in areas such as autonomous driving, mapping, and localization. Place recognition identifies a place using query sensor data and a known database. One of the main challenges is to develop a model that can deliver accurate results while being robust to environmental variations. We propose two multi-modal place recognition models, namely PRFusion and PRFusion++. PRFusion utilizes global fusion with manifold metric attention, enabling effective interaction between features without requiring camera-LiDAR extrinsic calibrations. In contrast, PRFusion++ assumes the availability of extrinsic calibrations and leverages pixel-point correspondences to enhance feature learning on local windows. Additionally, both models incorporate neural diffusion layers, which enable reliable operation even in challenging environments. We verify the state-of-the-art performance of both models on three large-scale benchmarks. Notably, they outperform existing models by a substantial margin of +3.0 AR@1 on the demanding Boreas dataset. Furthermore, we conduct ablation studies to validate the effectiveness of our proposed methods. The codes are available at: https://github.com/sijieaaa/PRFusion

• The paper introduces two models, PRFusion and PRFusion++, that integrate image and point cloud data to enhance place recognition without relying on extrinsic calibrations.
• It employs global fusion with manifold metric attention and neural diffusion layers to ensure smooth feature interaction and robust predictions.
• Extensive evaluations reveal a +3.0 AR@1 improvement on the Boreas dataset, demonstrating the models' superior performance in challenging conditions.

The paper "PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion" explores the development of advanced models for place recognition, a critical component in areas like autonomous driving, mapping, and localization. The authors introduce two innovative models: PRFusion and PRFusion++, which aim to achieve high accuracy and robustness in recognizing places using both image and point cloud data.

Key Contributions and Techniques

• PRFusion Model: This model employs global fusion with manifold metric attention. This technique allows for effective interaction between features from images and point clouds without needing camera-LiDAR extrinsic calibrations. The model focuses on creating a robust representation by integrating information across modalities.
• PRFusion++ Model: Building on PRFusion, PRFusion++ assumes the availability of extrinsic calibrations. It uses pixel-point correspondences for enhanced feature learning. This local window-based feature enhancement ensures a finer level of detail in place recognition, optimizing performance in complex environments.
• Neural Diffusion Layers: Both models incorporate neural diffusion layers. These layers facilitate reliable operation in challenging environments with varying conditions by enabling smooth diffusion of features across the network, ensuring consistency and robustness in predictions.

Performance and Validation

The authors demonstrate the state-of-the-art performance of PRFusion and PRFusion++ through comprehensive evaluations on three large-scale benchmarks. Notably, the models show a significant improvement, with a +3.0 AR@1 increase on the challenging Boreas dataset, underscoring their competitiveness and effectiveness compared to existing models.

Ablation Studies

Ablation studies were conducted to validate the impact of various design choices and methods within the models. These studies confirm the effectiveness of their proposed approaches, highlighting the substantive contribution of each component in improving overall model performance.

Practical Implications

The availability of the models' codes on GitHub makes them accessible for further research and practical applications, paving the way for advancements in the fields of robotics and computer vision where reliable place recognition is crucial.

In summary, the paper presents innovative solutions for multi-modal place recognition, addressing both accuracy and robustness through advanced model designs and integration techniques, and establishes a new benchmark standard in the field.