Autonomous Navigation

109 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 )

Most implemented papers

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

facebookresearch/habitat-api 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

lexfridman/deeptraffic 9 Jan 2018

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.

Towards real-time unsupervised monocular depth estimation on CPU

mattpoggi/pydnet 29 Jun 2018

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 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.

Learning to Navigate in Cities Without a Map

deepmind/streetlearn 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.

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

pulp-platform/pulp-dronet 4 May 2018

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

bborja/wasr_network 7 Jan 2020

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 24 Feb 2020

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 ECCV 2020

In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

gnns4hri/sngnnv2 17 Feb 2021

This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms.