VOLoc: Visual Place Recognition by Querying Compressed Lidar Map (2402.15961v1)

Abstract: The availability of city-scale Lidar maps enables the potential of city-scale place recognition using mobile cameras. However, the city-scale Lidar maps generally need to be compressed for storage efficiency, which increases the difficulty of direct visual place recognition in compressed Lidar maps. This paper proposes VOLoc, an accurate and efficient visual place recognition method that exploits geometric similarity to directly query the compressed Lidar map via the real-time captured image sequence. In the offline phase, VOLoc compresses the Lidar maps using a \emph{Geometry-Preserving Compressor} (GPC), in which the compression is reversible, a crucial requirement for the downstream 6DoF pose estimation. In the online phase, VOLoc proposes an online Geometric Recovery Module (GRM), which is composed of online Visual Odometry (VO) and a point cloud optimization module, such that the local scene structure around the camera is online recovered to build the \emph{Querying Point Cloud} (QPC). Then the QPC is compressed by the same GPC, and is aggregated into a global descriptor by an attention-based aggregation module, to query the compressed Lidar map in the vector space. A transfer learning mechanism is also proposed to improve the accuracy and the generality of the aggregation network. Extensive evaluations show that VOLoc provides localization accuracy even better than the Lidar-to-Lidar place recognition, setting up a new record for utilizing the compressed Lidar map by low-end mobile cameras. The code are publicly available at https://github.com/Master-cai/VOLoc.

• The paper introduces VOLoc, a method that efficiently queries compressed LiDAR maps using geometric similarity and an attention-based aggregation module.
• It demonstrates reduced storage requirements and real-time capability by operating directly on compressed maps without decompression.
• The study leverages transfer learning on large LiDAR datasets to enhance 6DoF pose estimation, outperforming conventional VPR methods on the KITTI dataset.

Visual Place Recognition in Compressed Lidar Maps with VOLoc

Introduction

Visual Place Recognition (VPR) is pivotal for several applications such as autonomous driving, augmented reality, and robotic navigation. Traditional VPR methods, which primarily rely on image-to-image querying, often suffer from low accuracy due to challenges like environmental changes in lighting or season. With the advancement of Lidar technologies, researchers started to explore image-to-Lidar and Lidar-to-Lidar place recognition to overcome these challenges. However, one significant obstacle is the vast storage required for city-scale Lidar maps, which necessitates compression that, in turn, complicates direct place recognition. Addressing this, the presented paper introduces VOLoc—a method that efficiently queries compressed Lidar maps using images by exploiting geometric similarity.

VOLoc Framework

VOLoc stands out by its ability to operate directly on compressed Lidar maps without the need for decompression. The framework encompasses two key phases:

• Offline Phase: In this stage, VOLoc compresses Lidar maps employing a Geometry-Preserving Compressor (GPC). GPC optimizes storage by clustering and downsampling while preserving the maps' geometric structure. This process is vital for maintaining the possibility for accurate 6DoF pose estimation.
• Online Phase: During this phase, the Geometric Recovery Module (GRM) reconstructs the local scene geometry around the camera in real-time by generating a Querying Point Cloud (QPC). The QPC, once compressed using GPC, is converted into a global descriptor by an attention-based aggregation module. This descriptor is then used to query the compressed Lidar map.

Notably, VOLoc incorporates transfer learning to enhance the aggregation network's accuracy and generality, utilizing a large Lidar point cloud dataset for pre-training and fine-tuning on VO-generated point clouds.

Empirical Evaluation

Extensive evaluations were conducted to assess VOLoc against existing VPR methods. Utilizing the KITTI dataset, the paper demonstrated VOLoc's commendable localization accuracy, outperforming or equating with state-of-the-art Lidar-to-Lidar place recognition methods. VOLoc achieved such results with notably reduced query sizes and map storage requirements, emphasizing its practicality for devices with limited storage or bandwidth capabilities.

Outcomes and Insights

The investigation into VOLoc reveals several key insights:

• Geometric Similarity: Leveraging geometric similarity enables VOLoc to bridge the gap between images and compressed Lidar maps effectively.
• Storage Efficiency: By operating on compressed Lidar maps, VOLoc offers a solution that significantly reduces storage requirements.
• Real-time Capability: Despite the compression and decompression processes, VOLoc can facilitate real-time place recognition, making it viable for dynamic applications like autonomous driving or mobile navigation.
• Transfer Learning Efficiency: The adoption of a transfer learning scheme enhances the network's ability to comprehend geometric features, thereby improving localization accuracy.

Future Directions

The promising results of VOLoc pave the way for further exploration in VPR. Immediate extensions could investigate the applicability to single-image queries, potentially expanding VOLoc's utility. Further, the paper underscores the importance of optimized geometric recovery and compression techniques, suggesting an avenue of research focused on enhancing these components for improved efficiency and accuracy.

Conclusion

VOLoc marks a significant advancement in the domain of visual place recognition by introducing an efficacious method to query compressed Lidar maps using real-time captured images. Through judicious use of geometric similarity, compression algorithms, and an attention-based aggregation module, VOLoc establishes a new benchmark for memory-efficient and accurate place recognition. As future work progresses in refining these techniques, VOLoc's foundational framework provides a robust starting point for evolving VPR methodologies to be more adept and efficient in handling the complexities of real-world navigation and mapping applications.