Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.
翻译:活跃的同步定位和绘图(SLAM)是规划和控制机器人运动以建立最准确和最完整的周围环境模型的问题。自从30多年前出现了第一次积极认识的基本工作以来,这个领域在不同的科学界日益受到越来越多的关注。这带来了许多不同的方法和配方,对新的和有经验的研究人员来说都是必要和极其宝贵的当前趋势进行了审查。在这项工作中,我们调查了活跃的SLAM的最新技术,并深入地审视了为满足现代应用的需要仍然需要关注的公开挑战。我们从历史角度提出了统一的问题提法,并审查了完善的模块解决方案,将问题分为三个阶段,确定、选择和执行潜在的导航行动。我们随后分析了其他方法,包括信仰空间规划和深层增强学习技术,并审查了多机器人协调的相关工作。手稿最后讨论了新的研究方向,讨论了可再研究、积极空间认识和实际应用等问题。