Autonomous Navigation
131 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
Are socially-aware trajectory prediction models really socially-aware?
An attack is a small yet carefully-crafted perturbations to fail predictors.
Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
Semantic maps represent the environment using a set of semantically meaningful objects.
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection
The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground.
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
Using our dataset and annotations, we release benchmarks for 3D object detection and 3D semantic segmentation using established metrics.
Encouraging LSTMs to Anticipate Actions Very Early
In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos.
Automatic Discovery and Geotagging of Objects from Street View Imagery
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations.
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
Conditional Affordance Learning for Driving in Urban Environments
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.
Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures
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.
