Multimodal Sensing, Fusion, and VLMs for Scene Understanding and Robot Vision
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 2427
Special Issue Editors
Interests: computer vision; pattern recognition; object detection tracking; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: robotic platform design; optimal mechanism; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in multimodal sensing and vision–language models (VLMs/MLLMs) are reshaping scene understanding and robot vision. This Special Issue explicitly welcomes work that centers on sensors and their applications, including the following: (i) sensor design/selection (RGB, depth, thermal, event, LiDAR, radar, IMU, microphones), placement, and synchronization; (ii) data acquisition pipelines (ground-truthing, calibration, time/pose alignment, distortion compensation, uncertainty/quality assessment); (iii) fusion architectures that are sensor-aware (modality reliability weighting, failure detection, cross-modal registration); and (iv) deployment on real systems (embedded/edge platforms, power/latency tradeoffs, safety).
We encourage submissions that report new datasets and benchmarks built from real sensor suites, application-driven case studies (e.g., mobile/industrial robots, assistive and field robotics), and evaluation protocols that quantify calibration, robustness under domain shift, and reliability in-the-wild. Algorithmic contributions (e.g., open-vocabulary perception, grounded VQA, VLA policies) are welcome when grounded in sensor usage—through realistic data collection, sensor-specific calibration, or hardware-in-the-loop experiments. Our goal is to advance scalable, safe, and explainable robot vision by tightly coupling sensing, fusion, and VLM-driven perception in practical environments.
Prof. Dr. ByoungChul Ko
Dr. Sungkeun Yoo
Guest Editors
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Keywords
- multimodal sensing and fusion (RGB/Depth/Thermal/Event/LiDAR/Radar/IMU/Audio)
- sensor calibration, synchronization, and uncertainty modeling
- Vision–Language Models (VLMs)/Multimodal LLMs (MLLMs) for sensor-grounded perception
- open-vocabulary detection/segmentation/tracking with real sensor data
- grounded VQA, referring expressions, spatio-temporal/3D grounding
- 3D perception and SLAM with language guidance
- cross-modal registration
- edge/embedded deployment, real-time inference, power–latency tradeoffs
- robustness, safety, OOD detection, and trustworthy evaluation
- retrieval-augmented and knowledge-aware perception with sensor priors
- datasets, data acquisition protocols, and reproducible benchmarks
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