# Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments
## 1 Introduction  
Localization of autonomous robots has been a very active research field for many years, and it is becoming more relevant due to the great increase in their industrial use [1]. More recently, aerial robotic technologies have proliferated in numerous sectors, and their market is expected to keep growing [2]. Several market studies [3] have already identified that inspection is one of the major business cases for aerial robots in the near future. For a long time, aerial robots have made use of Global Navigation Satellite System (GNSS) technologies for outdoor applications, thanks to the existence of numerous robust and low-cost autopilots that allow their autonomous navigation while satellite signals are available. With the advances in the technology and maturity of these solutions, there are other applications that involve the operation of aerial robots in indoor environments such as warehouses [4], or in the Gas&Oil or civil infrastructure sectors, where multiple obstacles and occlusions degrade GNSS signals (see Figure 1). This requires additional technologies to be developed at a similar maturity level as their current outdoor counterparts. There are numerous algorithms used for localizing ground robots in an efficient and robust manner [5, 6]. However, in the case of aerial robots, which move and operate in 3D environments, these methods imply greater computational complexity. Apart from the spatial dimensionality, drones are exposed to additional challenges as their onboard capabilities are limited in terms of payload, computational resources, and power constraints. One approach for localizing aerial robots indoors, which is widely adopted in research activities, is the use of motion capture systems. They allow very accurate tracking for a broad variety of developments [7, 8]. These systems are composed of a network of cameras that are strategically positioned to provide a working volume where the aerial platform can operate. However, these systems require a direct line of sight between the tracked platform and a minimum number of cameras to guarantee good performance; hence, they are not suitable for cluttered environments or dynamic scenarios. Moreover, they are not cost-effective when scaling to bigger environments. Vision-based approaches for robot localization are very popular because of the affordability, availability, and low weight of cameras. It is very common to find algorithms that use monocular cameras fused with inertial sensors to perform Simultaneous Localization And Mapping (SLAM) [9; 10; 11; 12]. Drawbacks of this type of technique when applied to aerial robots are tightly related to the high-speed movements of these platforms, or when they navigate in big empty spaces such as industrial scenarios. Some authors proposed the use of stereovision or RGB-D sensors [13; 14; 15; 16] for improving monocular approaches by adding depth measurements in the form of point clouds from the environment, but the practical range when integrating them into aerial robots is usually limited to a few meters, which could be insufficient in most industrial settings. The main problem with vision-based approaches is that they do not provide a robust solution for aerial robots, due to their low reliability in the long term, not only due to the accumulated drift but also to other factors affecting cameras, such as lighting. LiDAR (Light Detection and Ranging) is another sensing alternative to generate localization solutions based on point clouds. This research line has attracted a lot of attention recently, especially in the automotive sector [17]. There are existing algorithms that can be used for computing odometry and mapping from LiDAR data [18; 19]. Their limitations arise when integrating such approaches into aerial robots, due to the presence of vibrations and/or fast motions, which could generate drift that prevents the system from being robust in the long term.