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Found 7 papers, 6 papers with code
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set.
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.
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.
Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning
Learning visuomotor control policies in robotic systems is a fundamental problem when aiming for long-term behavioral autonomy.
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
Our experimental results, on traversals of the Oxford RobotCar dataset with no GPS data, show that MVP can achieve 53% and 93% navigation success rate using VO and RO, respectively, compared to 7% for a vision-only method.
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.
CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning
While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity.
