Sensor Fusion
88 papers with code • 0 benchmarks • 2 datasets
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]
Benchmarks
These leaderboards are used to track progress in Sensor Fusion
Datasets
Most implemented papers
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
We define the failures (e. g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors.
The ApolloScape Open Dataset for Autonomous Driving and its Application
In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving.
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
How should representations from complementary sensors be integrated for autonomous driving?
Stein ICP for Uncertainty Estimation in Point Cloud Matching
Quantification of uncertainty in point cloud matching is critical in many tasks such as pose estimation, sensor fusion, and grasping.
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
Our application problem is the monitoring of lake ice on Alpine lakes.
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion
The proposed solution enables superior performance under various distortion levels in detection over current state-of-art methods.
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation.
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars.