Visual Navigation
Leaderboards
Greatest papers with code
Visual Representations for Semantic Target Driven Navigation
We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.
Cognitive Mapping and Planning for Visual Navigation
CVPR 2017
• tensorflow/models
• 
The accumulated belief of the world enables the agent to track visited regions of the environment.
Scaling and Benchmarking Self-Supervised Visual Representation Learning
ICCV 2019
• facebookresearch/fair_self_supervision_benchmark
• 
Self-supervised learning aims to learn representations from the data itself without explicit manual supervision.
Are We Making Real Progress in Simulated Environments? Measuring the Sim2Real Gap in Embodied Visual Navigation
We find that SRCC for Habitat as used for the CVPR19 challenge is low (0. 18 for the success metric), which suggests that performance improvements for this simulator-based challenge would not transfer well to a physical robot.
An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers.
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average.
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
CVPR 2019
• chihyaoma/regretful-agent
•
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making.
DECISION MAKING VISION-LANGUAGE NAVIGATION VISUAL NAVIGATION
The Regretful Navigation Agent for Vision-and-Language Navigation
CVPR 2019 (Oral) 2019
• chihyaoma/regretful-agent
•
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making.
DECISION MAKING VISION-LANGUAGE NAVIGATION VISUAL NAVIGATION
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
ICLR 2019
• chihyaoma/selfmonitoring-agent
•
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
NATURAL LANGUAGE VISUAL GROUNDING VISION-LANGUAGE NAVIGATION VISUAL NAVIGATION