Efficient Multi-Camera Visual-Inertial SLAM for Micro Aerial Vehicles

Visual SLAM is an area of vivid research and bears countless applications for moving robots. In particular, micro aerial vehicles benefit from visual sensors due to their low weight. Their motion is, however, often faster and more complex than that of ground-based robots which is why systems with multiple cameras are currently evaluated and deployed. This, in turn, drives the computational demand for visual SLAM algorithms. We present an extension of the recently introduced monocular ORB-SLAM for multiple cameras alongside an inertial measurement unit (IMU). Our main contributions are: Embedding the multi-camera setup into the underlying graph SLAM approach that defines the upcoming sparse optimization problems on several adjusted subgraphs, integration of an IMU filter that supports visual tracking, and enhancements of the original algorithm in local map estimation and keyframe creation. The SLAM system is evaluated on a public stereo SLAM dataset for flying robots and on a new dataset with three mounted cameras. The main advantages of the proposed method are its restricted computational load, high positional accuracy, and low number of parameters.