Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices
arXiv:2103.01994 · doi:10.1109/ACCESS.2022.3196389
Abstract
Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.
7 pages, currently under-review
References in corpus (7)
- Convolutional Neural Network-based Place Recognition
- Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions
- Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments
- STA-VPR: Spatio-temporal Alignment for Visual Place Recognition
- Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?
- DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
- ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments