Autonomous Navigation
130 papers with code • 0 benchmarks • 5 datasets
Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.
( Image credit: Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars )
Benchmarks
These leaderboards are used to track progress in Autonomous Navigation
RELLIS-3D
JRDB
IN2LAAMA
Most implemented papers
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
facebookresearch/habitat-api
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ICLR 2020
We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation
We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process.
Learning to Navigate in Cities Without a Map
deepmind/streetlearn
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NeurIPS 2018
We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.
Towards real-time unsupervised monocular depth estimation on CPU
mattpoggi/pydnet
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To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.
Social NCE: Contrastive Learning of Socially-aware Motion Representations
vita-epfl/social-nce
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ICCV 2021
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
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.
A water-obstacle separation and refinement network for unmanned surface vehicles
Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV).
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
lzccccc/SMOKE
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Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving.
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
HarshayuGirase/PECNet
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ECCV 2020
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
RELLIS-3D Dataset: Data, Benchmarks and Analysis
The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography.
