SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition (2102.11603v2)

Abstract: Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment. While initial VPR systems used simple direct image methods or hand-crafted visual features, recent work has focused on learning more powerful visual features and further improving performance through either some form of sequential matcher / filter or a hierarchical matching process. In both cases the performance of the initial single-image based system is still far from perfect, putting significant pressure on the sequence matching or (in the case of hierarchical systems) pose refinement stages. In this paper we present a novel hybrid system that creates a high performance initial match hypothesis generator using short learnt sequential descriptors, which enable selective control sequential score aggregation using single image learnt descriptors. Sequential descriptors are generated using a temporal convolutional network dubbed SeqNet, encoding short image sequences using 1-D convolutions, which are then matched against the corresponding temporal descriptors from the reference dataset to provide an ordered list of place match hypotheses. We then perform selective sequential score aggregation using shortlisted single image learnt descriptors from a separate pipeline to produce an overall place match hypothesis. Comprehensive experiments on challenging benchmark datasets demonstrate the proposed method outperforming recent state-of-the-art methods using the same amount of sequential information. Source code and supplementary material can be found at https://github.com/oravus/seqNet.

• The paper introduces a hierarchical framework, SeqNet, that improves visual place recognition by integrating learned sequential descriptors.
• It employs temporal convolution networks to encode image sequences, outperforming methods like DenseVLAD and NetVLAD in extreme conditions.
• SeqNet significantly reduces computational overhead while achieving high recall rates, benefiting real-time robotic navigation and mapping.

SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition

The research paper presents a novel framework for enhancing Visual Place Recognition (VPR) through a sequential descriptor system named SeqNet. The fundamental contribution of the paper is its hierarchical approach to VPR, which emphasizes the integration of short learnt sequential descriptors into a comprehensive pipeline for place recognition.

In VPR tasks, the system aims to match real-time visual information with previously mapped images under varying environmental conditions. Traditional VPR systems relied largely on direct image comparisons or hand-crafted features, but contemporary approaches leverage learned features to improve recognition accuracy. However, the paper identifies a notable gap: existing methods, while proficient in image-level feature learning, struggle with sequential order information, a critical aspect when considering a mobile robot's trajectory through an environment.

The authors propose a hybrid solution that incorporates temporal convolution networks to generate sequential descriptors. SeqNet forms the core of this mechanism, utilizing 1-D temporal convolutions to encode sequences of images, thereby establishing a ranked list of potential location matches. These hypotheses are subsequently refined using sequential score aggregation techniques with single image descriptors.

Key experimental results underscore the method's strength. Comprehensive benchmarks demonstrate SeqNet's superiority over recent state-of-the-art methods using comparable sequential data volumes. Notably, SeqNet outperforms other descriptor models, such as DenseVLAD and NetVLAD, especially in scenarios characterized by extreme environmental variability, such as day vs. night cycles and seasonal changes. Additionally, SeqNet performs remarkably well in challenging datasets like Nordland and Mapillary Street Level Sequences (MSLS), exhibiting strong generalization across different urban environments and weather conditions.

The paper also explores the impact of sequence length and temporal description on computational efficiency. SeqNet not only provides improved accuracy but also reduces computational demands significantly compared to traditional full-database sequence matching methods. These improvements have direct implications for mobile robotics, enabling real-time place recognition with higher recall rates and reduced latency.

Future directions could focus on further optimizing SeqNet's learning framework to predictively enhance descriptors’ responsiveness to sequential filtering requirements. Additionally, extending these methods to more complex environments and developing more versatile descriptor models will be vital in addressing application-specific challenges, such as those encountered in autonomous navigation and advanced mapping technologies.

The paper concludes by emphasizing the dual benefits of SeqNet: a reduction in computational overhead alongside improved recognition accuracy. This balance is critical for broad applications in robotics, mapping, and navigation, making SeqNet a significant step forward in VPR technology. The provision of publicly available code underscores the authors' commitment to fostering further research and development in this field.