MICRO-RADAR AND UWB AIDED UAV NAVIGATION IN GNSS DENIED ENVIRONMENT

During the last decade, the number of applications of UAVs has continuously increased, making the global UAV market one of those with the highest rate of growth. The worldwide increasing usage of UAVs is causing an ever-growing demand for efficient solutions in order to make them usable in every kind of working condition. In fact, nowadays the main restriction to the usage of UAVs is probably the need of reliable position estimates provided by using the Global Navigation Satellite System (GNSS): since UAVs mostly rely on the integration of GNSS/Inertial Navigation System (INS) to properly fulfil their tasks, they face a major challenge while navigating in GNSS denied environments. The goal of this paper is that of investigating possible strategies to reduce such main restriction to UAV usage, i.e. enabling flights in GNSS denied environment by providing position estimates with accuracy quite comparable to that of standard GNSS receivers currently mounted on commercialized drones. To be more specific, this paper proposes the combined use of a novel Frequency Modulated Continuous Wave (FMCW) Radar, a set of Ultra-Wideband (UWB) devices, and Inertial Measurement Unit (IMU) measurements in order to compensate for the unavailability of the GNSS signal units. A 24-GHz micro FMCW radar and a UWB device have been attached to a quadcopter during the flight. The radar receives the reflections from ground scatterers, whereas the UWB system provides range measurements between a UWB rover mounted on the UAV and a set of UWB anchors distributed along the flying area

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

UWB-based system for UAV Localization in GNSS-Denied Environments: Characterization and Dataset

Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion capture systems are typically utilized indoors when accurate localization is needed. However, these systems are expensive and most require a fixed setup. Recently, visual-inertial odometry and similar methods have advanced to a point where autonomous UAVs can rely on them for localization. The main limitation in this case comes from the environment, as well as in long-term autonomy due to accumulating error if loop closure cannot be performed efficiently. For instance, the impact of low visibility due to dust or smoke in post-disaster scenarios might render the odometry methods inapplicable. In this paper, we study and characterize an ultra-wideband (UWB) system for navigation and localization of aerial robots indoors based on Decawave's DWM1001 UWB node. The system is portable, inexpensive and can be battery powered in its totality. We show the viability of this system for autonomous flight of UAVs, and provide open-source methods and data that enable its widespread application even with movable anchor systems. We characterize the accuracy based on the position of the UAV with respect to the anchors, its altitude and speed, and the distribution of the anchors in space. Finally, we analyze the accuracy of the self-calibration of the anchors' positions.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020

A novel outlier removal method for two-dimensional radar odometry

Autonomous navigation of platforms in complex environments has a key role in many applications. However, the environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional radar odometry, based on their range rate and bearing angle. The range rate and bearing angle are in fact combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for Sense-and-Avoid purposes. The method has been tested in simulated scenario to show effectiveness in detecting outliers and in real case scenario to demonstrate significant improvement in reconstruction of trajectory of platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy

A Comparison Between Uwb and Laser-based Pedestrian Tracking

Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.Peer reviewe

Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching

This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching between aerial images and a map database. By using semantic segmentation, the ground objects are labelled and the configuration of the objects is used to find the corresponding location in the map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem. This paper proposes a pattern matching algorithm which does not require altitude information or a camera model to estimate the absolute horizontal position. The feasibility analysis with simulated images shows the proposed map-based navigation can be realized with the proposed pattern matching algorithm and it is able to provide positions given the labelled objects.Comment: 6 pages, 4 figure

A Study on UWB-Aided Localization for Multi-UAV Systems in GNSS-Denied Environments

Unmanned Aerial Vehicles (UAVs) have seen an increased penetration in industrial applications in recent years. Some of those applications have to be carried out in GNSS-denied environments. For this reason, several localization systems have emerged as an alternative to GNSS-based systems such as Lidar and Visual Odometry, Inertial Measurement Units (IMUs), and over the past years also UWB-based systems. UWB technology has increased its popularity in the robotics field due to its high accuracy distance estimation from ranging measurements of wireless signals, even in non-line-of-sight measurements. However, the applicability of most of the UWB-based localization systems is limited because they rely on a fixed set of nodes, named anchors, which requires prior calibration. In this thesis, we present a localization system based on UWB technology with a built-in collaborative algorithm for the online autocalibration of the anchors. This autocalibration method, enables the anchors to be movable and thus, to be used in ad-doc and dynamic deployments. The system is based on Decawave's DWM1001 UWB transceivers. Compared to Decawave's autopositioning algorithm we drastically reduce the calibration time while increasing accuracy. We provide both experimental measurements and simulation results to demonstrate the usability of this algorithm. We also present a comparison between our UWB-based and other non-GNSS localization systems for UAVs positioning in indoor environments