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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 276–300 of 589 papers

TitleStatusHype
Polarimetric image augmentation—0
Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR Comparative Study—0
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation—0
Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning—0
Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles—0
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm—0
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image—0
PriorMotion: Generative Class-Agnostic Motion Prediction with Raster-Vector Motion Field Priors—0
PRISM: Preference Refinement via Implicit Scene Modeling for 3D Vision-Language Preference-Based Reinforcement Learning—0
Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds—0
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction—0
Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments—0
Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems—0
QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning—0
R2P: A Deep Learning Model from mmWave Radar to Point Cloud—0
Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car—0
RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles—0
RAIL: Robust Acoustic Indoor Localization for Drones—0
Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention—0
Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models—0
Real-Time Navigation for Autonomous Aerial Vehicles Using Video—0
Real-Time Sensor-Based Feedback Control for Obstacle Avoidance in Unknown Environments—0
Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review—0
Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review—0
Rapid Exploration for Open-World Navigation with Latent Goal Models—0
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