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：主动同时定位与地图构建(Active SLAM)是指规划和控制机器人运动以构建最准确、最完整的周围环境模型的问题。自三十多年前主动感知领域出现首项基础性工作以来，该领域在不同科学界中获得了越来越多的关注。这催生了众多不同的方法和理论框架，因此对当前研究趋势进行综述对新手和资深研究者而言都极具必要性和价值。本文系统梳理了主动SLAM领域的研究现状，深入剖析了为满足现代应用需求仍需关注的开放挑战。在提供历史视角后，我们提出了统一的问题表述框架，并回顾了成熟的模块化解耦方案——该方案将问题分解为识别、选择和执行潜在导航动作三个阶段。随后我们分析了包括置信空间规划和深度强化学习技术在内的替代方法，并综述了多机器人协调的相关研究。本文最后讨论了新的研究方向，涵盖可复现研究、主动空间感知及实际应用等主题。