In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present two approaches for coordinating a system consisting of multiple robots to perform Active Collaborative SLAM (AC-SLAM) for environmental exploration. Our two coordination approaches, synchronous and asynchronous implement a methodology to prioritize robot goal assignments by the central server. We also present a method to efficiently spread the robots for maximum exploration while keeping SLAM uncertainty low. Both coordination approaches were evaluated through simulation and experiments on publicly available datasets, rendering promising results.

The objective is to find the optimal state vector that minimizes the measurement error between the estimated pose and environmental landmarks. Most SLAM algorithms are passive, i.e., the robot is controlled manually and the navigation or path planning algorithm does not actively take part in robot motion or trajectory. Active SLAM (A-SLAM), however, tries to solve the optimal exploration problem of the unknown environment by proposing a navigation strategy that generates future goal/target positions actions which decrease map and pose uncertainties, thus enabling a fully autonomous navigation and mapping SLAM system without the need of an external controller or human effort. In Active Collaborative SLAM (AC-SLAM) multiple robots interchange information to improve their localization estimation and map accuracy to achieve some high-level tasks such as exploration. The exchanged information can be localization information [1], entropy [2], visual features [3], and frontier points [4]. In this article, we present a multi-agent AC-SLAM system for efficient environment exploration using frontiers detected over an Occupancy Grid (OG) map. In particular, in this work, we aim at: 1. Extending the A-SLAM approach of [5] which uses a computationally inexpensive D-optimality criterion for utility computation to a multi-agent AC-SLAM framework.