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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
Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions—0
Autonomous Systems: Autonomous Systems: Indoor Drone Navigation—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
BadODD: Bangladeshi Autonomous Driving Object Detection Dataset—0
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm—0
Beyond Segmentation: Road Network Generation with Multi-Modal LLMs—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models—0
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation—0
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Building3D: A Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Building Intelligent Autonomous Navigation Agents—0
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles—0
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach—0
Can You Trust Your Pose? Confidence Estimation in Visual Localization—0
Capsule Network Performance with Autonomous Navigation—0
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates—0
Collection: Datasets from AFAR Challenge—0
Comparison of Deep Space Navigation Using Optical Imaging, Pulsar Time-of-Arrival Tracking, and/or Radiometric Tracking—0
Computational complexity reduction of deep neural networks—0
Connecting Visual Experiences using Max-flow Network with Application to Visual Localization—0
Context-aware 6D Pose Estimation of Known Objects using RGB-D data—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories—0
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation—0
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