# Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments
## 1 Introduction
Another family of approaches that cope with long-term localization solutions relies on previously built maps of the environment, taking advantage of the fact that industrial applications usually take place in a specific setting. In addition, map-based approaches for localization are usually better in terms of reliability and computational requirements. The map can be computed offline using cameras and/or depth sensors, making use of existing algorithms. Monte Carlo localization (MCL) is an approach that makes use of a known map of the environment, commonly used for robot navigation in indoor environments [20]. It is a probabilistic localization algorithm that makes use of a particle filter to estimate the pose of the robot within the map, based on sensor measurements. Important benefits include the possibility of accommodating arbitrary sensor characteristics, motion dynamics, and noise distributions. There is an adaptive version of MCL, AMCL, which evaluates and adapts the number of particles for improving the computational load. An open-source version of AMCL is available in the Robot Operating System (ROS) ([[http://wiki.ros.org/amcl](http://wiki.ros.org/amcl)]([http://wiki.ros.org/amcl](http://wiki.ros.org/amcl))  
Figure 1: Aerial robots are becoming more relevant for industrial applications in GNSS-denied or GNSS-degraded environments. (accessed on 26 April 2021)); however, it is meant for wheeled robots moving in a 2D environment, requiring a 2D laser scanner for map building and localization. Other authors have proposed the use of 3D LiDAR data in MCL, but simplifying the 3D scans as 2D slices for autonomous ground vehicles [21]. To the best of our knowledge, there is no AMCL solution specifically designed for aerial robots. Localization based on radio beacons has also been deeply studied in the last few years [22]. Radiofrequency sensors provide point-to-point distance measurements and offer a low-cost solution that can be applied in almost any scenario, with the advantage that a direct line of sight is not required between each pair of sensors. Ultra-wide band (UWB) is a wireless communication technology that has attracted the interest of the research community as a promising solution for accurate localization and tracking of targets. It is particularly suitable for applications in GNSS-denied environments, using several fixed sensors placed at known positions in the environment, plus a sensor onboard the aerial robot [23; 24]. Nevertheless, these sensors are not suitable for constituting a complete localization system, due to the lack of orientation information, which leads to multiple localization hypotheses. The aforementioned approaches are promising in the sense that they can all provide solutions to the localization problem, but there are major drawbacks if they are used separately. The presented work builds on previous research from the authors [25], a multi-modal sensor fusion approach which is based on an enhanced MCL algorithm that integrates an odometry source for short-term estimations with other sources of 3D measurements, namely 3D point clouds of the environment, a previously built 3D map, and radio-based sensors installed in the environment and localized within such a 3D map. The main contribution of the presented work focuses on a similar combination of technologies, but with a stronger focus on achieving higher accuracy and robustness for the safe introduction of aerial robots in industrial applications, thanks to the use of a 3D LiDAR sensor. To understand the improvement related to the change of sensor, Table 1 can be observed. It must be taken into account that the environment where the algorithm is intended to be applied will make it more meaningful to use one sensor or another. In our case, an industrial environment involves very wide spaces, so the range is decisive. The sensor change also makes sense in the field of precision, since it is evident that the algorithm will provide better results when the inputs on which it acts are more precise.