**1. Introduction**

In recent years, there has been growing interest in autonomous UAS, and this is also reflected in the increase in scientific publications on the topic [1–5]. The importance of this research area is fundamental since the development of autonomous technologies in drones can guarantee significant benefits for society in the near future. For instance, implementations for the search of missing people and the exploration of post-disaster or inaccessible environments to humans where the use of drones with autonomous decisionmaking capabilities could play a decisive factor in terms of the number of lives saved [6,7].

Nowadays, the topic of human transport in urban contexts through the use of autonomous drones is also becoming more and more interesting, precisely because it would allow to limit ground traffic by moving part of it to the sky. In this regard, the work presented in this article was developed within the European project AURORA (Safe Urban Air Mobility for European Citizens), which has as its ultimate goal the development of autonomous technologies aimed at this type of use. In this regard, a fundamental element that an autonomous driving system must implement on board is the obstacle-detection and avoidance part [8,9]. In fact, without it, the aircraft cannot modify the global trajectory to avoid unexpected obstacles and therefore would not be able to be used for applications that instead require high safety and flexibility in terms of automatic recalculation of the desired trajectory. In the literature, there are papers that deal with the development of such systems; many of these are based on the use of stereoscopic cameras capable of depth perception, allowing to carry out the detection of obstacles [10,11].

**Citation:** Bigazzi, L.; Miccinesi, L.; Boni, E.; Basso, M.; Consumi, T.; Pieraccini, M. Fast Obstacle Detection System for UAS Based on Complementary Use of Radar and Stereoscopic Camera. *Drones* **2022**, *6*, 361. https://doi.org/10.3390/ drones6110361

Academic Editor: Anastasios Dimou

Received: 29 October 2022 Accepted: 16 November 2022 Published: 18 November 2022

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Recently, the scientific community has begun to experiment with the use of radar systems, which have multiple advantages, such as longer detection ranges and being insensitive to light and visibility conditions [12,13]. Compared to optical systems, it is important to note that radar technology also has disadvantages, such as a lower spatial resolution and the almost total absence of resolution in height [12,14]. However, the two technologies have complementary characteristics and therefore lend themselves well to being used together; in fact, they are able to compensate for each other's disadvantages. Automotive applications of simultaneous radar and vision systems for obstacle detection [15–17] are widely documented in the literature; however, this is not common in the UAS field. In fact, except for some implementations where the 3D perception is obtained by merging together the radar data with the monocular vision [14], until now and to the authors' knowledge, there are no documented techniques that use radar and stereoscopic vision in a complementary way.

In this article, an approach for UAS applications that exploits the complementary features of an automotive-derived radar and a stereoscopic optical sensor to increase the reliability of the detection algorithm is presented.

The two systems have been kept completely independent in such a way that a malfunction of one cannot affect the functioning of the other. As it will be detailed in the following sections, the avoidance strategy comes into operation as soon as one of the two systems detects a dangerous obstacle according to the detection policy implemented.

The paper is organized as follows: Section 2 describes the hardware and software architecture, explains the method of creating environmental maps and defines the algorithm that has the task of detecting dangerous obstacles inside them. Furthermore, Section 2 also discusses the implemented avoidance strategy which comes into operation only when obstacles considered dangerous are highlighted. Section 3 shows the results obtained from field tests carried out in an open environment that simulates an urban context. Finally, Section 4 reports the discussion on results and future developments.

#### **2. Materials and Methods**
