## 7 Conclusions  
We proposed a real-time embedded localization system for ground robots called FAST-FUSION. Our system is composed of an omnidirectional extension of the state-of-the-art monocular VO method Libviso2 that uses raw omnidirectional images, a LIDAR to calculate the motion scale, and a gyroscope to support the estimation in pure rotations. Our VO approach works with raw omnidirectional camera images using a state-of-the-art camera model to consider the lens/mirror distortion. The standalone VO approach is publicly available at the official Libviso2 repository ([[https://github.com/srv/viso2](https://github.com/srv/viso2)]([https://github.com/srv/viso2](https://github.com/srv/viso2))). We also propose a GPU-based optimization for Raspberry Pi that uses OpenCL to increase the FAST-FUSION frame rate. To test our solution, we created three open-source datasets using our robotic platform. In short, we achieved real-time performance in an embedded configuration,and our system presented higher accuracy than the original Libviso2 approach. Our system can be implemented considering the following low-cost hardware configuration: a Raspberry Pi 3B (30E), a Raspberry Pi fisheye camera (35.85E), a UM7 Orientation sensor (148.88E), and a RPLIDAR A2M8 360 Degree Laser Scanner (330.00E).  
In a future work, instead of using low-level features such as blobs and corners, high-level features, also known as landmarks, will be used. With this evolution a SLAM approach will substitute the current monocular VO method used in FAST-FUSION.  
Conceptualization, A.A. and F.B. and A.S.; methodology, A.A. and F.B.; software, A.A.; validation, F.B., A.J.S. and L.S.; formal analysis, F.B. and A.J.S.; investigation, A.A. and F.S. and A.A. and L.S.; resources, F.B. and L.S.; data curation, L.S.; writing-original draft preparation, A.A.; writing-review and editing, A.A. and F.B. and A.J.S.; funding acquisition, F.S.  
This research was funded by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation--COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project \"ROMOVI: POCI-01-0247-FEDER-017945\". The opinions included in this paper shall be the sole responsibility of their authors. The European Commission and the Authorities of the Programme aren't responsible for the use of information contained therein.  
The authors acknowledge the comments and suggestions from the anonymous reviewers and the assistant editor [PERSON] for helping improving the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VO & Visual odometry \\ SLAM & Simultaneous localization and mapping \\ API & Application programming interface \\ KF & Kalman filter \\ SVD & Single value decomposition \\ CPU & Central processing unit \\ GPU & Graphical processing unit \\ QPU & Quad processing unit \\ ROS & Robot operating system \\ OpenCL & Open computing language \\ GNSS & Global navigation satellite systems \\ LIDAR & Light detection and ranging \\ DSO & Direct sparse odometry \\ SVO & Semi-direct visual odometry \\ SIFT & Scale-invariant feature transform \\ UML & Unified modeling language \\ \end{tabular}