Adriano Mancini

A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks

In this paper a vision-based approach for guidance and safe landing of an Unmanned Aerial Vehicle (UAV) is proposed. The UAV is required to navigate from an initial to a final position in a partially known environment. The guidance system allows a remote user to define target areas from a high resolution aerial or satellite image to determine either the waypoints of the navigation trajectory or the landing area. A feature-based image-matching algorithm finds the natural landmarks and gives feedbacks to an onboard, hierarchical, behaviour-based control system for autonomous navigation and landing. Two algorithms for safe landing area detection are also proposed, based on a feature optical flow analysis. The main novelty is in the vision-based architecture, extensively tested on a helicopter, which, in particular, does not require any artificial landmark (e.g., helipad). Results show the appropriateness of the vision-based approach, which is robust to occlusions and light variations.

A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications

The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results.

Feature group matching for appearance-based localization

Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization tasks. In this paper, we address the issues of appearance-based topological and metric localization by introducing a novel group matching approach to select less but more robust features to match the current robot view with reference images. Feature group matching is based on the consideration that feature descriptors together with spatial relations are more robust than classical approaches. Our datasets, each consisting of a large number of omnidirectional images, have been acquired over different day times (different lighting conditions) both in indoor and outdoor environments. The feature group matching outperforms the SIFT in indoor localization showing better performances both in the case of topological and metric localization. In outdoor SURF remains the best feature extraction method, as reported in literature.

Customers’ activity recognition in intelligent retail environments

This paper presents a multimedia system for the modeling and visualization of drama heritage. The system consists of an ontology based annotation schema for the dramatic metadata of the cultural heritage artifacts (in textual or audiovisual form), a web–based platform for the introduction of the metadata, and a module for the visualization and exploration of such metadata. The system was tested on the cross–media studies of drama.

Autonomous navigation, landing and recharge of a quadrotor using artificial vision

In this paper a solution to UAV reduced endurance and autonomous flight is proposed. With a complete on-board solution, based on artificial vision, the developed system is able to autonomously take off, navigate and land, recharging its battery by using a dedicated landing platform, both in indoor and outdoor scenarios. The landing platform includes a passive centering system to correct the landing error of the UAV, with a novel design wich reduce cost and increase the safety (thanks to small and isolated electrical contacts) without invasive hardware changes on the drone. The developed vision algorithm provides a fast and accurate measurement of UAV position with respect to the landing platform using a visual target, but at the same time it is able to automatically switch to an estimation of position that is independent from the visual target. This aspect is used during navigation or when the tracking of the target fails, ensuring a continuous position measurement feed to the controllers. The developed control system manages all the different phases of a mission (motor turning on/off, take off, navigation, landing, ...) with low control error, ensuring a landing over the landing platform with an error that is lower than 5cm for both x and y axis. The developed software in ROS environment is modular and provides input/output interfaces to receive command, or send data.

A Framework for Simulation and Testing of UAVs in Cooperative Scenarios

Today, Unmanned Aerial Vehicles (UAVs) have deeply modified the concepts of surveillance, Search&Rescue, aerial photogrammetry, mapping, etc. The kinds of missions grow continuously; missions are in most cases performed by a fleet of cooperating autonomous and heterogeneous vehicles. These systems are really complex and it becomes fundamental to simulate any mission stage to exploit benefits of simulations like repeatability, modularity and low cost. In this paper a framework for simulation and testing of UAVs in cooperative scenarios is presented. The framework, based on modularity and stratification in different specialized layers, allows an easy switching from simulated to real environments, thus reducing testing and debugging times, especially in a training context. Results obtained using the proposed framework on some test cases are also reported.

A framework for simulations and tests of mobile robotics tasks

This paper presents an education framework, developed in Matlab, for studying and experimenting typical mobile robotics tasks such as obstacle avoidance, localization, navigation and SLAM. The most important characteristic of this framework is the ability to easily switch from a simulator to a real robot to tune and test algorithms and to evaluate results in simulated and real environments. The framework is being used with interesting results in robotic courses at the Universita Politecnica delle Marche in Ancona, Italy. In the second part of the paper a test case to evaluate an optimization of a Monte Carlo localization process with sonar sensors is presented

Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network

The aim of this paper is to present an integrated system consisted of a RGB-D camera and a software able to monitor shoppers in intelligent retail environments. We propose an innovative low cost smart system that can understand the shoppers’ behavior and, in particular, their interactions with the products in the shelves, with the aim to develop an automatic RGB-D technique for video analysis. The system of cameras detects the presence of people and univocally identifies them. Through the depth frames, the system detects the interactions of the shoppers with the products on the shelf and determines if a product is picked up or if the product is taken and then put back and finally, if there is not contact with the products. The system is low cost and easy to install, and experimental results demonstrated that its performances are satisfactory also in real environments.

Vision-based autonomous navigation and landing of an unmanned aerial vehicle using natural landmarks

This paper presents the design and implementation of a vision-based navigation and landing algorithm for an autonomous helicopter. The vision system allows to define target areas from a high resolution aerial or satellite image to determine the waypoints of the navigation trajectory or the landing area. The helicopter is required to navigate from an initial position to a final position in a partially known environment using GPS and vision, to locate a landing target (a helipad of a known shape or a natural landmark) and to land on it. The vision system, using a feature-based image matching algorithm, finds the area and gives feedbacks to the control system for autonomous landing. Vision is used for accurate target detection, recognition and tracking. The helicopter updates its landing target parameters owing to vision and uses an on board behavior-based controller to follow a path to the landing site. Results show the appropriateness of the vision-based approach that does not require any artificial landmark (e.g., helipad) and is quite robust to occlusions, light variations and seasonal changes (e.g., brown or green leaves).

Low cost embedded system for increasing retail environment intelligence

The success of pervasive smart environments lies in the capacity to involve visitors to interact with them. It is essential for retail stores. In this paper we describe the setting-up of a low cost system for the indoor localization and customer interaction, developed with a complex infrastructure of wireless embedded sensors. The creation of a responsive store allows customers to connect the real world to their smart devices and will overcome the lack of ubiquity in public spaces; furthermore, from in-venue analytics and proximity sensor it is possible to customize the user experience. First of all we describe the whole sensor network. We go in deep into the active beacon technology adopted for this study. Then, thanks to the analytics, we present a data evaluation with the aim of determining the best sensor arrangement, according to several user tests. Beside the strong enhancement of human interaction, the results of our essay demonstrate how embedded localization systems could be a useful source for data collection beside the strong enhancement of human interaction. This paper is focused to help retailers and insiders for many purposes such as products development or improvement, segmentation strategies and human behaviour analyses into such stores where the embedded computing augment the environment.