C2VIR-SLAM: Centralized Collaborative Visual-Inertial-Range Simultaneous Localization and Mapping

Collaborative simultaneous localization and mapping have a great impact on various applications such as search-and-rescue and agriculture. For each agent, the key to performing collaboration is to measure the motion relative to other participants or external anchors; currently, this is mainly accompanied by (1) matching to the shared maps from other agents or (2) measuring the range to anchors with UWB devices. While requiring multiple agents to visit the same area can decrease the task efficiency and anchors demand a distribution process, this paper proposes to use a monocular camera, an inertial measurement unit (IMU), and a UWB device as the onboard sensors on each agent to build an accurate and efficient centralized collaborative SLAM system. For each participant, visual-inertial odometry is adopted to estimate the motion parameters and build a local map of the explored areas. The agent-to-agent range is measured by the onboard UWB and is published to the central server together with the estimated motion parameters and the reconstructed maps. We designed a global optimization algorithm to make use of the cross-agent map match information detected by a visual place technique, and the agent-to-agent range information to optimize the motion parameter of all the participants and merge the local maps into a global map. Compared with existing collaborative SLAM systems, the proposed system can perform collaboration with onboard UWB measurements only, vision only, and a combination of these; this greatly improves the adaptiveness and robustness of the collaborative system. We also present an in-depth analysis of C2VIR-SLAM in multiple UAV real-flight datasets.

Automated summary

Collaborative simultaneous localization and mapping (SLAM) plays a crucial role in various applications. This paper proposes a centralized collaborative SLAM system using a monocular camera, an inertial measurement unit (IMU), and a UWB device as onboard sensors. The system utilizes visual-inertial odometry for motion estimation and local map construction, and incorporates a global optimization algorithm to merge the local maps into a global map using cross-agent map match information and agent-to-agent range measurements.