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Autonomous Navigation

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 )

Papers

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Showing 426–450 of 589 papers

TitleStatusHype
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections—0
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter—0
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation—0
Navigating Conceptual Space; A new take on Artificial General Intelligence—0
Navigational Path-Planning For All-Terrain Autonomous Agricultural Robot—0
Navigation in a simplified Urban Flow through Deep Reinforcement Learning—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images—0
NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot—0
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection—0
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review—0
Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking—0
Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge—0
NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization—0
New Methods for Detecting Concentric Objects With High Accuracy—0
NFL-BA: Improving Endoscopic SLAM with Near-Field Light Bundle Adjustment—0
Nighttime Dehaze-Enhancement—0
Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity—0
OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios—0
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
OIL: Observational Imitation Learning—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments—0
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