A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

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 article, 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 multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

I. Introduction

Autonomous operation in robotics applications requires robots to have access to a consistent model of the surrounding environment, in order to support safe planning and decision making. Toward this goal, a robot must have the ability to create a map of the environment, localize itself on it, and control its own motion. Active simultaneous localization and mapping (SLAM) refers to the joint resolution of these three core problems in mobile robotics, with the ultimate goal of creating the most accurate and complete model of an unknown environment. Active SLAM can be seen as a decision-making process in which the robot has to choose its own future control actions, balancing between exploring new areas and exploiting those already seen to improve the accuracy of the resulting map model.