Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a real-time method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating the route using a GPS-equipped vehicle and building a compact database of simple visual and 3D features. We then localize using a Bayesian filter to match sequences of visual or range measurements to the database. The algorithm is reliable across wide environmental changes, including lighting differences, seasonal variations, and occlusions, achieving an average localization accuracy of 1 m over an 8 km route. The method converges correctly even with wrong initial position estimates solving the kidnapped robot problem.

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Real-time topometric localization

Vision-based topological mapping and localization methods

One of the fundamental requirements of an autonomous vehicle is the ability to determine its location on a map. Frequently, solutions to this localization problem rely on GPS information or use expensive three dimensional (3D) sensors. In this paper, we describe a method for long-term vehicle localization based on visual features alone. Our approach utilizes a combination of topological and metric mapping, which we call topometric localization, to encode the coarse topology of the route as well as detailed metric information required for accurate localization. A topometric map is created by driving the route once and recording a database of visual features. The vehicle then localizes by matching features to this database at runtime. Since individual feature matches are unreliable, we employ a discrete Bayes filter to estimate the most likely vehicle position using evidence from a sequence of images along the route. We illustrate the approach using an 8.8 km route through an urban and suburban environment. The method achieves an average localization error of 2.7 m over this route, with isolated worst case errors on the order of 10 m.

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Visual topometric localization

Indoor localization of mobile agents using wireless technologies is becoming very important in military and civil applications. This paper introduces an approach for the indoor localization of a mini UAV based on Ultra-WideBand technology, low cost IMU and vision based sensors. In this work an Extended Kalman Filter (EKF) is introduced as a possible technique to improve the localization. The proposed approach allows to use a low-cost Inertial Measurement Unit (IMU) in the prediction step and the integration of vision-odometry for the detection of markers nearness the touchdown area. The ranging measurements allow to reduce the errors of inertial sensors due to the limited performance of accelerometers and gyros. The obtained results show that an accuracy of 10 cm can be achieved.

An IMU/UWB/Vision-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment using 802.15.4a Wireless Sensor Network

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.

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

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.

Feature group matching for appearance-based localization

This paper addresses the problem of long term mobile robot localization in large urban environments using a partial apriori knowledge made by different kind of images. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where tall nearby structures obstruct the view of the satellites. In our work, we use omnidirectional vision-based sensors to complement GPS and odometry and provide accurate localization.We also present some novel Monte Carlo Localization optimizations and we introduce the concept of online knowledge acquisition and integration presenting a framework able to perform long term robot localization in real environments. The vision system identifies prominent features in the scene and matches them with a database of geo-referenced features already known (with a partial coverage of the environment and using both directional and omnidirectional images and with different resolutions) or learned and integrated during the localization process (omnidirectional images only). Results of successful robot localization in the old town of Fermo are presented. The whole architecture behaves well also in long term experiments, showing a suitable and good system for real life robot applications with a particular focus on the integration of different knowledge sources.

Robot localization in urban environments using omnidirectional vision sensors and partial heterogeneous apriori knowledge

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 present a mobile robot localization system based on local feature matching of omnidirectional images. In particular, we address the issues of appearance-based topological localization by comparing common feature-extractor methods(SIFT and SURF) to select robust features to match the current robot view with reference images. Our datasets, each consisting of a large number of omnidirectional images, have been acquired over different day times (different lighting conditions) and dynamic content in large outdoor environments (over 80.000 m2). Two different approaches (WTA and MCL) were used to evaluate performances, which, in general, are satisfactory. In particular, the use of Monte Carlo particle filtering improves topological localization results for all datasets with all algorithms.

Robot localization using omnidirectional vision in large and dynamic outdoor environments

During last years Linux started to climb the market of operating systems (OSs), and Ubuntu, derived by Debian OS, has become a good alternative to common OSs like Windows XP or Vista. The mobile robotics scientific community makes use of Linux based OSs to avoid the lack of stability that affects Microsoft OSs, especially when real time conditions must be satisfied. In this paper we present the Linux distribution RoboBuntu, acronym formed by the union of ROBOt and uBUNTU, to overcome the almost totally independent robotic software platforms existing today. The key idea behind RoboBuntu is the integration of different tools for mobile robotics into an embedded Ubuntu distribution. Another important characteristics of RoboBuntu is that every “hard step”, like installation and configuration of OS and tools, is hidden to common users. In particular, RoboBuntu can be used either by students or researchers, as LiveCd, permanent installation on standard hard drive or, more interesting, on a USB storage flash disk.

Andrea Ascani

Papers

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

Feature group matching for appearance-based localization

Robot localization in urban environments using omnidirectional vision sensors and partial heterogeneous apriori knowledge

Robot localization using omnidirectional vision in large and dynamic outdoor environments

RoboBuntu: A Linux distribution for mobile robotics