DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition

Marvin Chancán Michael Milford
QUT

NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving (ML4AD)

Abstract

Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise similarity matrix between reference and query image sequences of a single route - to further reduce false-positive rates compared to single-frame retrieval methods. As a result, performing multi-frame place recognition can be extremely slow for deployment on autonomous vehicles or evaluation on large datasets, and fail when using relatively short parameter values such as a sequence length of 2 frames. In this paper, we propose DeepSeqSLAM: a trainable CNN+RNN architecture for jointly learning visual and positional representations from a single monocular image sequence of a route. We demonstrate our approach on two large benchmark datasets, Nordland and Oxford RobotCar - recorded over 728 km and 10 km routes, respectively, each during 1 year with multiple seasons, weather, and lighting conditions. On Nordland, we compare our method to two state-of-the-art sequence-based methods across the entire route under summer-winter changes using a sequence length of 2 and show that our approach can get over 72% AUC compared to 27% AUC for Delta Descriptors and 2% AUC for SeqSLAM; while drastically reducing the deployment time from around 1 hour to 1 minute against both.

Preprint: [PDF] ArXiv: [ABS] Code: [GitHub]

News

(Mar 3, 2021) Update codebase Readme.md file.
(Mar 1, 2021) Related manuscript on Sequential Place Learning is now available.
(Oct 30, 2020) Paper DeepSeqSLAM accepted at the NeurIPS 2020 Workshop on ML4AD.

Relevant (Extended) Paper

Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition

Marvin Chancán

YouTube Video

SlidesLive @NeurIPS 2020

Bibtex

@article{chancan2021spl,
  title = {Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition},
  author = {Marvin Chanc{\'a}n and Michael Milford},
  journal = {arXiv preprint arXiv:2103.02074},
  year = {2021}
}

@article{chancan2020deepseqslam,
  title = {DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition},
  author = {Marvin Chanc{\'a}n and Michael Milford},
  journal = {arXiv preprint arXiv:2011.08518},
  year = {2020}
}