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Autonomous Navigation

18 papers with code · Computer Vision
Subtask of Autonomous Vehicles

Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.

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Greatest papers with code

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

CVPR 2018 charlesq34/pointnet

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

3D OBJECT DETECTION AUTONOMOUS NAVIGATION FEATURE ENGINEERING OBJECT LOCALIZATION

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

9 Jan 2018lexfridman/deeptraffic

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.

AUTONOMOUS DRIVING AUTONOMOUS NAVIGATION Q-LEARNING

An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs

10 May 2019pulp-platform/pulp-dronet

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.

AUTONOMOUS NAVIGATION VISUAL NAVIGATION

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

4 May 2018pulp-platform/pulp-dronet

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.

AUTONOMOUS NAVIGATION VISUAL NAVIGATION

Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

15 Sep 2019hku-mars/loam_livox

LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.

AUTONOMOUS NAVIGATION

Learning to Navigate in Cities Without a Map

NeurIPS 2018 deepmind/streetlearn

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.

AUTONOMOUS NAVIGATION

Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures

6 Jul 2018neka-nat/probreg

Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality.

AUTONOMOUS NAVIGATION POINT CLOUD REGISTRATION SCENE RECOGNITION

Towards real-time unsupervised monocular depth estimation on CPU

29 Jun 2018mattpoggi/pydnet

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.

AUTONOMOUS NAVIGATION IMAGE RECONSTRUCTION MONOCULAR DEPTH ESTIMATION

Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

20 Feb 2018wang-chen/KCC

Robust velocity and position estimation is crucial for autonomous robot navigation.

AUTONOMOUS NAVIGATION OPTICAL FLOW ESTIMATION ROBOT NAVIGATION

Conditional Affordance Learning for Driving in Urban Environments

18 Jun 2018xl-sr/CAL

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs.

AUTONOMOUS DRIVING AUTONOMOUS NAVIGATION IMITATION LEARNING