自动驾驶

Continuous Target-free Extrinsic Calibration of a Multi-Sensor System from a Sequence of Static Viewpoints

Philipp Glira, Christoph Weidinger, Johann Weichselbaum

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Self-Supervised Scene Flow Estimation with 4-D Automotive Radar

Fangqiang Ding, Zhijun Pan, Yimin Deng, Jianning Deng, Chris Xiaoxuan Lu

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments Copyright (c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref IEEE Robotics and Automation Letters (RA-L), 2022

The Hilti SLAM Challenge Dataset

Michael Helmberger, Kristian Morin, Beda Berner, Nitish Kumar, Giovanni Cioffi, Davide Scaramuzza

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments in IEEE Robotics and Automation Letters, 2022

Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications

Christopher Grimm, Tai Fei, Ernst Warsitz, Ridha Farhoud, Tobias Breddermann, Reinhold Haeb-Umbach

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV、cs.AI

Learning Selective Sensor Fusion for States Estimation

Changhao Chen, Stefano Rosa, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, Andrew Markham

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS). arXiv admin note: text overlap with arXiv:1903.01534

Raw High-Definition Radar for Multi-Task Learning

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV、eess.IV

Comments 12 pages, 7 figures, 6 tables

Journal ref CVPR2022

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

Jie Yin, Ang Li, Tao Li, Wenxian Yu, Danping Zou

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments accepted by IEEE RA-L

Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation

Nimet Kaygusuz, Oscar Mendez, Richard Bowden

专题命中 多传感器融合 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

Journal ref 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 2944-2949

Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles

Simon Bultmann, Jan Quenzel, Sven Behnke

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments Accepted for: 10th European Conference on Mobile Robots (ECMR), Bonn, Germany, September 2021

Two Headed Dragons: Multimodal Fusion and Cross Modal Transactions

Rupak Bose, Shivam Pande, Biplab Banerjee

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV、eess.IV

Comments Accepted in IEEE International conference on Image Processing (ICIP), 2021

3D Radar Velocity Maps for Uncertain Dynamic Environments

Ransalu Senanayake, Kyle Beltran Hatch, Jason Zheng, Mykel J. Kochenderfer

专题命中 多传感器融合 ：occupancy(abstract)；分类 cs.RO、cs.CV

Comments Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping

Ozan Çatal, Wouter Jansen, Tim Verbelen, Bart Dhoedt, Jan Steckel

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.AI

Comments Accepted for publication at IEEE ICRA2021

Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization

Peng Gao, Rui Guo, Hongsheng Lu, Hao Zhang

专题命中 多传感器融合 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

Comments Revise several typos and change the Fig2 to be more illustrative

FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization

Nishant Kumar, Stefan Gumhold

专题命中 多传感器融合 ：autonomous driving(abstract)；分类 cs.CV、eess.IV

Comments 30 pages, 9 figures, MDPI Journal (Computers)

There and Back Again: Learning to Simulate Radar Data for Real-World Applications

Rob Weston, Oiwi Parker Jones, Ingmar Posner

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments 6 pages + 2 references

Robust building footprint extraction from big multi-sensor data using deep competition network

Mehdi Khoshboresh-Masouleh, Mohammad R. Saradjian

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV、eess.IV

Comments 8 pages, 5 figures

Journal ref The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W18, 2019

Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision

David Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, Paul Newman

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments accepted for publication at the IEEE Intelligent Transportation Systems Conference (ITSC) 2020

Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots

Corneliu Arsene

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks

Ruochen Yin, Biao Yu, Huapeng Wu, Yutao Song, Runxin Niu

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV、cs.AI

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu

专题命中 多传感器融合 ：self-driving(abstract)；分类 cs.RO、cs.AI

Comments arXiv admin note: substantial text overlap with arXiv:1909.00895

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu

专题命中 多传感器融合 ：self-driving(abstract)；分类 cs.RO、cs.AI

Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion

Andreas Pfeuffer, Klaus Dietmayer

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Journal ref 22st International Conference on Information Fusion (FUSION) (2019)

Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment

Yizhou Wang, Gaoang Wang, Hung-Min Hsu, Hui Liu, Jenq-Neng Hwang

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV；autonomous driving(comments)

Comments 10 pages, 7 figures, 6 tables, CVPR 2021 Workshop on Autonomous Driving

RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completion

RbFT-Net：用于4D雷达-相机深度补全的融合前校正时间雷达锚点

Wentao Zhao, Shouxuan Wu, Yongtao Cen, Tianchen Deng, Yuyang Zhang, Jingchuan Wang

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.CV

AI总结 本文提出RbFT-Net框架，通过图像条件校正模块校正雷达锚点并选择性传播，解决雷达测量的稀疏与干扰问题，在相关数据集上的深度补全性能优于对比方法。

What Irregularity Costs: CUDA C++, Rust, and Triton on a Hash-Blocked GPU Workload

非规则性的代价：哈希阻塞GPU工作负载上的CUDA C++、Rust与Triton

Petr Korolev

机构 * Spacial Intelligence Labs（空间智能实验室） 纠错

专题命中 多传感器融合 ：occupancy(abstract)；分类 cs.CV

AI总结 该研究对比了CUDA C++、Rust、Triton在哈希阻塞GPU工作负载上的性能，发现规则阶段三者性能接近，非规则阶段Triton慢一个数量级，Rust接近CUDA，还分析了性能差异原因并修复了cuda-oxide的一个缺陷。

Comments 23 pages, 5 figures, 4 tables. Includes a correctness result for TSDF fusion implementations: at hash load factors reached by ordinary depth trajectories, the Triton implementation silently discards blocks. Code, raw measurement CSVs and an interactive viewer: https://github.com/realitymatrix/what-irregularity-costs

DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views

DerainSplat：基于稀疏雨天视图的前馈式干净3D高斯溅射重建

Fuzhen Jiang, Changyue Shi, Chuxiao Yang, Xinyuan Hu, Wenjie Ye, Minghao Chen

专题命中 多传感器融合 ：autonomous driving(abstract)；分类 cs.CV

AI总结 DerainSplat是一种前馈框架，可从少量雨天视图重建干净3D场景，通过四阶段合成数据集与天气网络等设计，在多类数据集上优于现有方法且泛化性强。

Dense Soft Weighting for Radar Ego-Velocity Estimation

用于雷达自速度估计的密集软加权方法

Atar Babgei, Chenyu Zhao, Michael Breza, Julie A. McCann

机构 * Imperial College London（帝国理工学院） 纠错

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO

AI总结 针对视觉退化环境中雷达自速度估计的传统CFAR方法易丢失有效线索的问题，提出密集软加权雷达前端，结合鲁棒加权最小二乘估计自速度，在多数据集上显著降低位姿误差且可实时运行。

Comments Submitted to

Neural Surface and Reflectance Modelling from 3D Radar Data

从3D雷达数据进行神经表面和反射率建模

Judith Treffler, Vladimír Kubelka, Henrik Andreasson, Martin Magnusson

机构 * Örebro University（厄勒布鲁大学） 纠错

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO

AI总结 提出一种神经隐式方法，从雷达点云联合建模场景几何和视角相关雷达强度，利用混合特征编码学习连续SDF，生成更平滑准确的3D表面重建。

Comments Accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026

DiffRadar: Differentiable Physics-Aware Radar SLAM with Gaussian Fields

DiffRadar：基于高斯场的可微物理感知雷达SLAM

Gaurav Bagwe, Xiaoyong Yuan, Yongji Wu, Lan Zhang

机构 * Clemson University（克莱姆森大学） 纠错

专题命中 多传感器融合 ：LiDAR(abstract)；分类 cs.RO

AI总结 研究针对现有雷达SLAM系统不足，提出DiffRadar，将雷达观测建模为可微高斯场，通过可微雷达前向模型联合优化位姿与场景结构，经实验验证该方法能显著提升轨迹精度、地图一致性并保持实时性能。

Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping

毫米波雷达：从合成孔径到概率地图

Jui-Te Huang, Ruoyang Xu, Michael Kaess

机构 * School of Computer Science, Robotics Institute, Carnegie Mellon University（卡内基梅隆大学计算机科学学院机器人研究所） 纠错

专题命中 多传感器融合 ：occupancy(abstract)；分类 cs.RO

AI总结 针对毫米波雷达数据创建概率地图的挑战，建立从原始信号到概率占用地图的完整流程，含合成孔径雷达处理与概率建模，经室内验证、性能分析及参数研究，展示方法有效性与局限性，并贡献开源数据集及处理管道。