自动驾驶

Fast Obstacle Avoidance Motion in SmallQuadcopter operation in a Cluttered Environment

Chaitanyavishnu S. Gadde, Mohitvishnu S. Gadde, Nishant Mohanty, Suresh Sundaram

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

Comments Submitted and Accepted to IEEE Conecct 2021

CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations

A. Ouaknine, A. Newson, J. Rebut, F. Tupin, P. Pérez

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

Comments 9 pages, 5 figues. Accepted at ICPR 2020. Erratum: results in Table III have been updated since the ICPR proceedings, models are selected using the PP metric instead of the previously used PR metric

UNIFY: Multi-Belief Bayesian Grid Framework based on Automotive Radar

Stefan Haag, Bharanidhar Duraisamy, Daniel Pfrommer, Wolfgang Koch, Martin Fritzsche, Jurgen Dickmann

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

Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data

Markus Horn, Ole Schumann, Markus Hahn, Jürgen Dickmann, Klaus Dietmayer

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

Comments 6 pages, 6 figures, 1 table

Journal ref 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?

Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot

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

Comments Accepted for publication in ICRA/RA-L 2021. Version 3

BiFNet: Bidirectional Fusion Network for Road Segmentation

Haoran Li, Yaran Chen, Qichao Zhang, Dongbin Zhao

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

PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module

Liang Xie, Chao Xiang, Zhengxu Yu, Guodong Xu, Zheng Yang, Deng Cai, Xiaofei He

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

Comments 8 pages, 5 figures

Probably Unknown: Deep Inverse Sensor Modelling In Radar

Rob Weston, Sarah Cen, Paul Newman, Ingmar Posner

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

Comments 6 full pages, 1 page of references

Guided Lensless Polarization Imaging

引导式无镜头偏振成像

Noa Kraicer, Erez Yosef, Raja Giryes

机构 * Tel Aviv University（特拉维夫大学） ； School of Electrical Engineering, Faculty of Engineering（工程学院电气工程学院） 纠错

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

AI总结 本文提出一种结合紧凑型偏振-RGB传感器和常规RGB相机的无镜头偏振成像系统，通过两阶段流程提升重建质量与通用性。

A Unified Cloud-Edge-Terminal Framework for Multimodal Integrated Sensing and Communication

多模态感知与通信一体化的统一云-边-终端框架

Yubo Peng, Luping Xiang, Kun Yang, Feibo Jiang, Kezhi Wang, Christos Masouros

专题命中 多传感器融合 ：autonomous driving(abstract)；LiDAR(abstract)

AI总结 本文提出统一云-边-终端框架，通过多模态感知与通信融合，解决异构融合、通信开销和系统扩展性等挑战，提升任务导向的多模态感知能力。

DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations

Shouyi Lu, Huanyu Zhou, Guirong Zhuo, Xiao Tang

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

Comments 9 pages,5 figures

mmWave Radar-Based Non-Line-of-Sight Pedestrian Localization at T-Junctions Utilizing Road Layout Extraction via Camera

Byeonggyu Park, Hee-Yeun Kim, Byonghyok Choi, Hansang Cho, Byungkwan Kim, Soomok Lee, Mingu Jeon, Seong-Woo Kim

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

GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion

Karlo Koledić, Luka Petrović, Ivan Marković, Ivan Petrović

机构 * University of Zagreb Faculty of Electrical Engineering and Computing（Zagreb大学电子工程与计算学院） 纠错

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

Comments ICCV 2025

PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications

Trisanth Srinivasan, Santosh Patapati

机构 * Cyrion Labs（塞里昂实验室） 纠错

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

Comments Accepted at IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2025 (CVPRW)

Robust sensor fusion against on-vehicle sensor staleness

Meng Fan, Yifan Zuo, Patrick Blaes, Harley Montgomery, Subhasis Das

机构 * Zoox Inc（Zoox公司） 纠错

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

Comments This paper has been accepted by CVPR 2025 Precognition Workshop

RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments

Zelin Meng, Takanori Fukao

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

Comments 7 pages, 2 figures

Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning

Ukcheol Shin, Kyunghyun Lee, Jean Oh

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

Comments Accepted at ICRA 2025, Github link: https://github.com/UkcheolShin/BridgeMultiSpectralDepth

ConceptFusion: Open-set Multimodal 3D Mapping

Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Alaa Maalouf, Shuang Li, Ganesh Iyer, Soroush Saryazdi, Nikhil Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso Miguel de Melo, Madhava Krishna, Liam Paull, Florian Shkurti, Antonio Torralba

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

Comments RSS 2023. Project page: https://concept-fusion.github.io Explainer video: https://www.youtube.com/watch?v=rkXgws8fiDs Code: https://github.com/concept-fusion/concept-fusion

Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition

Ross Greer, Mohan Trivedi

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

Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation

Hannah Schieber, Fabian Duerr, Torsten Schoen, Jürgen Beyerer

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

Comments conditionally accepted at IEEE IV 2022, 7 pages, 4 figures, 5 tables

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Dongxu Guo, Taylor Mordan, Alexandre Alahi

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

Comments Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2022

Multi-Agent Variational Occlusion Inference Using People as Sensors

Masha Itkina, Ye-Ji Mun, Katherine Driggs-Campbell, Mykel J. Kochenderfer

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

Comments 12 pages, 9 figures, International Conference on Robotics and Automation (ICRA) 2022

Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors

Dominik Notz, Felix Becker, Thomas Kühbeck, Daniel Watzenig

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

Comments 8 pages, 9 figures

Selective Sensor Fusion for Neural Visual-Inertial Odometry

Changhao Chen, Stefano Rosa, Yishu Miao, Chris Xiaoxuan Lu, Wei Wu, Andrew Markham, Niki Trigoni

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

Comments Accepted by CVPR 2019

Towards Self-Supervised High Level Sensor Fusion

Qadeer Khan, Torsten Schön, Patrick Wenzel

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

Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation

面向车辆侧倾角估计的不确定性感知混合机器学习虚拟传感器

Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Philipp Stauber, Michael Lange, Wolfgang Utschick, Michael Botsch

机构 * CARISSMA Institute of Automated Driving, Technische Hochschule Ingolstadt（CARISSMA自动化驾驶研究所，因戈尔施塔特技术大学） ； GeneSys Elektronik GmbH（GeneSys电子 GmbH） ； Technische Universität München（慕尼黑技术大学） 纠错

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

AI总结 本文提出不确定性感知混合学习架构，通过融合机器学习与车辆运动模型，提升车辆状态估计精度，同时引入新的数据集ReV-StED验证方法有效性。

Comments Accepted at the 2025 IEEE Intelligent Vehicles Symposium (IV)

Journal ref 2025 IEEE Intelligent Vehicles Symposium (IV)

Weather-Robust Scene Semantics with Vision-Aligned 4D Radar

具有视觉对齐4D雷达的抗天气场景语义

Kali Hamilton, Christoffer Heckman

机构 * Autonomous Robotics and Perception Group（自主机器人与感知组） 纠错

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

AI总结 本文提出通过冻结视觉语言模型将雷达编码器对齐于SigLIP嵌入，以提升恶劣天气下的场景语义理解，实验表明雷达配置在雾、轻雪和重雪条件下优于相机基线。

Comments 5 pages + references, 2 appendix pages. ICRA 2026 Radar in Robotics Workshop

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

Dr-BA：直接雷达束调整与定位的可分离优化

Daniil Lisus, Cedric Le Gentil, Timothy D. Barfoot

机构 * Robotics Institute, University of Toronto（多伦多大学机器人研究所） 纠错

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

AI总结 本文提出Dr-BA框架，通过直接处理2D旋转雷达强度图像实现雷达束调整与定位，利用雷达抗雨优势，解决稠密地图与传感器姿态联合估计问题，实现最先进的雷达束调整与跨会话定位性能。

Comments Accepted for presentation at RSS 2026

RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment

RadarSplat-RIO：基于高斯点分布的室内雷达-惯性里程计

Pou-Chun Kung, Yuan Tian, Zhengqin Li, Yue Liu, Eric Whitmire, Wolf Kienzle, Hrvoje Benko

机构 * Meta Reality Labs（Meta现实实验室） ； University of Michigan（密歇根大学） 纠错

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

AI总结 本文提出基于高斯点分布的雷达束调整框架，通过联合优化雷达传感器姿态和场景几何，显著减少位姿漂移并提升鲁棒性，实验表明在多个室内场景中，雷达束调整性能优于传统雷达-惯性里程计。

ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models

ETA-VLA：通过时间融合和内在LLM稀疏化实现高效标记适应的视觉-语言-动作模型

Yiru Wang, Anqing Jiang, Shuo Wang, Yuwen Heng, Zichong Gu, Hao Sun

机构 * Bosch Corporate Research, Bosch (China) Investment Ltd.（博世企业研究，博世（中国）投资有限公司） ； School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院） 纠错

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

AI总结 本文提出ETA-VLA框架，通过时间融合和内在LLM稀疏化技术，减少视觉-语言-动作模型的计算负担，实验表明在保持高精度的同时，显著降低计算量。