Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

This article presents a comprehensive review of the Active Simultaneous Localization and
Map** (A-SLAM) research conducted over the past decade. It explores the formulation …

Robot autonomous exploration is a challenging and valuable research field that has
attracted widespread research interest in recent years. However, existing methods often …

The autonomous map** of large-scale urban scenes presents significant challenges for
autonomous robots. To mitigate the challenges, global planning, such as utilizing prior GPS …

Purpose This paper aims to use the Monodepth method to improve the prediction speed of
identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the …

Currently, almost all robot state estimation and localization systems are based on the
Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF) …

Active simultaneous localization and map** (SLAM) is an important technique for mobile
robots to autonomously explore and map an environment. This letter considers the problem …

We present AVS-HRL, a modular Active visual SLAM system based on hierarchical
reinforcement learning. The reward function explicitly considers the efficiency of exploration …

Methods of simultaneous localization and map** (SLAM) are a solution for the navigation
problem of service robots. We present a graph SLAM system based on Hidden Markov …