Sequential Place Recognition
5 papers with code • 0 benchmarks • 0 datasets
State-of-the-art algorithms for route-based place recognition under changing conditions.
Benchmarks
These leaderboards are used to track progress in Sequential Place Recognition
Most implemented papers
A Hybrid Compact Neural Architecture for Visual Place Recognition
State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain.
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions.
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
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.
SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
oravus/seqNet
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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.
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade.
