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Sensor Fusion

Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. [Wikipedia]

Papers

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Showing 176–200 of 509 papers

TitleStatusHype
Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check—0
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion—0
Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving—0
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving—0
Deep learning empowered sensor fusion boosts infant movement classification—0
Harnessing Multimodal Sensing for Multi-user Beamforming in mmWave Systems—0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
All-day Depth Completion—0
Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics—0
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception—0
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—0
Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Clouds—0
GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position Estimation—0
Global Scale Self-Supervised Channel Charting with Sensor Fusion—0
Landmark Alternating Diffusion—0
Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion—0
Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System—0
Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition—0
ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions—0
In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review—0
Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging—0
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough RoadsCode0
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology—0
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