arXiv Machine Learning July 7, 2026 By Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris, Ioannis Kyriakides, Roberto Galeazzi

Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking

Read the original on arXiv Machine Learning →

arXiv:2603. 08457v2 Announce Type: replace-cross Abstract: Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

arXiv:2607. 22667v1 Announce Type: new Abstract: This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime sensor networks.

By Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris, Roberto Galeazzi, Ioannis Kyriakides

agents reinforcement-learning

arXiv AI

Jul 21

Li-ViP3D++: Query-Gated Deformable Camera-LiDAR Fusion for End-to-End Perception and Trajectory Prediction

arXiv:2601. 20720v2 Announce Type: replace-cross Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving.

By Matej Halinkovic, Nina Masarykova, Alexey Vinel, Marek Galinski

agents robotics multimodal safety

arXiv AI

Jul 28

UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation

arXiv:2602. 19349v2 Announce Type: replace-cross Abstract: LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode.

By Rohit Mohan, Florian Drews, Yakov Miron, Daniele Cattaneo, Abhinav Valada

llms computer-vision robotics multimodal benchmarks safety

arXiv AI

Aug 11

CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving

arXiv:2608. 09202v1 Announce Type: new Abstract: Modern autonomous vehicles are equipped with multiple sensors, such as cameras, LiDAR, and radar, for comprehensive environmental perception.

By Junyao Wang, Yulin Xu, Yu Li, Pramod Khargonekar, Mohammad Abdullah Al Faruque

llms agents robotics multimodal

arXiv AI

Jul 1

Information-Aided DVL Calibration

arXiv:2606. 31216v1 Announce Type: cross Abstract: The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous underwater vehicle (AUV) navigation solutions and, consequently, to mission success.

By Zeev Yampolsky, Itzik Klein

agents

arXiv AI

Aug 11

DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea

arXiv:2608. 08025v1 Announce Type: cross Abstract: Accurate 3D reconstruction of ships at sea is important for maritime supervision, damage assessment, and autonomous maritime operations.

By Jiaming Chen, Juntao Yang, Zhentao Zou, Qi Ming, Yi Yu, Zhihang Zhong, Xue Yang, Xue Jiang, Yue Zhou

agents

Related stories

Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

Li-ViP3D++: Query-Gated Deformable Camera-LiDAR Fusion for End-to-End Perception and Trajectory Prediction

UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation

CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving

Information-Aided DVL Calibration

DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea