6 accepted papers
Visual inertial odometry (VIO) serves as a cornerstone of environmental perception and spatial localization, with broad applications in autonomous driving, robotic navigation, and embodied intelligence. Although recent deep learning based VIO methods have achieved impressive accuracy and computation…
Multi-object tracking (MOT) plays a critical role in applications such as autonomous driving and surveillance. Camera-based approaches offer rich texture features for object association, while LiDAR-based methods provide accurate geometric information for spatial reasoning. Although each modality ad…
Visual-inertial odometry (VIO) is fundamental to autonomous driving, embodied navigation, and robotics. While geometry based methods dominate the field, learning based approaches are still in their infancy despite promising progress. Current deep learning based VIO methods usually fuse visual images
Ensuring the safety of various real-world applications based on reinforcement learning (RL), such as quadcopter control, robotic manipulators, and autonomous robots, remains a critical challenge, despite RL’s remarkable success in solving complex decision-making tasks. Existing on-policy Lagrangian
Multi-object tracking (MOT) plays a critical role in applications such as autonomous driving and surveillance. Camera-based approaches offer rich texture features for object association, while LiDAR-based methods provide accurate geometric information for spatial reasoning. Although each modality ad
Autonomous flight in unknown environments requires precise spatial and temporal trajectory planning, often involving computationally expensive nonconvex optimization prone to local optima. To overcome these challenges, we present the Neural-Enhanced Trajectory Planner (NEO-Planner), a novel approach