MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
This work addresses the need for reliable SLAM in applications like robotics and self-driving cars, but it is incremental as it builds upon an existing SLAM system.
The paper tackles the problem of robust, continuous camera pose estimation for multi-camera systems by extending a state-of-the-art SLAM system to handle arbitrary, rigidly coupled setups using the MultiCol model, and it demonstrates improved robustness compared to a single-camera version through performance evaluation on accurate ground truth.
The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w.r.t the observed environment (scene). In recent years many vision based systems that perform simultaneous localization and mapping (SLAM) have been presented and released as open source. In this paper, we extend and improve upon a state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model. In addition, we include a performance evaluation on accurate ground truth and compare the robustness of the proposed method to a single camera version of the SLAM system. An open source implementation of the proposed multi-fisheye camera SLAM system can be found on-line https://github.com/urbste/MultiCol-SLAM.