Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Inertial sensing is employed in a wide range of applications and platforms, from everyday
devices such as smartphones to complex systems like autonomous vehicles. In recent years …

Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …

Deep-learning-based localization and map** approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

Visual navigation in unknown environments poses significant challenges due to the
presence of many obstacles and low-texture scenes. These factors may cause frequent …

Reinforcement learning continuously optimizes decision-making based on real-time
feedback reward signals through continuous interaction with the environment …

This paper tackles the challenges of self-supervised monocular depth estimation in indoor
scenes caused by large rotation between frames and low texture. We ease the learning …

Reinforcement learning (RL) is effective for autonomous navigation tasks without prior
knowledge of the environment. However, traditional mobile robot navigation algorithms …