Global Positioning System (GPS) is intensively used for localization and navigation in mobile robotics due to its ease of use, its precision, and its worldwide accessibility. But in presence of tall obstacles (buildings) or vegetation (trees), the reception of GPS signals can be poor or impossible. Such a situation is not compatible with autonomous mobile robotics applications, and redundant localization tools need to be implemented. We present in this paper a solution for localization in mobile robotics based on PELICAN radar. PELICAN is a K-band panoramic radar, whose images are used to build radar maps of the traveled environments. The proposed solution realizes a real-time 3D matching between the current radar image and the pre-existing radar map, in order to estimate the position and the orientation of the radar within the map. Results obtained in localization and navigation are illustrated with a path-following application.

Robust environmental perception is a crucial parameter for the development of autonomous ground vehicle applications, especially in the field of agricultural robotics which is one of the priorities for the Horizon 2020 robotics funding (EU funding program for research and innovation). Because of uncontrolled and changing environmental conditions in outdoor and natural environments, data from optical sensors classically used in mobile robotics can be compromised and unusable. In such situations, microwave radar can provide an alternative and complementary solution for perception tasks. The aim of this paper is to present PELICAN radar, a microwave radar specifically designed for mobile robotics applications, including obstacle detection, mapping, and situational awareness in general. PELICAN radar provides each second a view over 360° of the surrounding environment, from 5 m up to 100 m. A description of the technological solutions adopted for the development of PELICAN radar (hardware and software) is presented. Results obtained in various outdoor and open environments, including urban and natural areas, are also described. Sets of PELICAN radar data are made available online for researchers interested in processing this type of data.

There is a growing need for lightweight airborne platforms that could provide precise information about the environment (topography, presence of obstacles, etc.) filling the data gap between aerial/satellite remote sensing and terrestrial systems. A major limitation of classical sensors such as vision or laser is that they are ineffective in degraded visual conditions. Millimeter-wave radar provides an alternative solution to overcome the shortcomings of optical solutions, because in the microwave range, data can be acquired independently of atmospheric conditions and time of the day. The intended application of a new radar sensor is the construction of digital elevation models of the overflown environments. As the design of new radar sensors for light airborne platforms is subject to specific technological constraints, a simulator of airborne radar surveys is developed. The objective of the simulator is to help the designer in defining the main parameters of the future airborne radar, and in developing radar signal processing algorithms.

Unmanned aerial vehicles are being increasingly used in challenging applications, such as environmental monitoring and surveying, precision agriculture, and mitigation actions in disaster sites. Visual cameras constitute an important component of a UAV sensor suite, allowing the vehicle to perceive the surrounding environment, and perform tasks such as mapping, 3D reconstruction, and path planning. In this work, a simulator reproducing the output of a visual camera transported onboard a UAV is proposed. The objective of the simulator is to assist the user in defining the most suitable camera configuration before going through field development and testing. Special focus is given to the realistic rendering of the environment. In this paper, first, the main elements of the simulator, including environment modeling, vehicle trajectory and camera modeling are described. Then, the use of the simulator to analyze the influence of some main camera parameters on the output of the imaging process is shown. Finally, the application of the simulator for the generation of aerial mosaics using Google Earth data is presented.

Robust environmental perception is a crucial parameter for the development of autonomous ground vehicle applications, especially in the field of agricultural robotics which is one of the priorities for the Horizon 2020 robotics funding (EU funding program for research and innovation). Because of uncontrolled and changing environmental conditions in outdoor and natural environments, data from optical sensors classically used in mobile robotics can be compromised and unusable. In such situations, millimeter-wave radar can provide an alternative and complementary solution for perception tasks. The aim of this paper is to present the PELICAN radar, a millimeter-wave radar specifically designed for mobile robotics applications, including obstacle detection, mapping and situational awareness in general. In this first of a two-part paper, the choice of a frequency-modulated continuous-wave radar is explained and the theoretical elements of this solution are detailed. PELICAN radar is using a rotating fan-beam antenna, and the construction of 2D representations of the surrounding environments with radar data is described through simulation results. The second part of the paper will be devoted for a detailed description of PELICAN radar, as well as experimental results.

In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate and robust environmental perception is a critical requirement to address unsolved issues including safe interaction with field workers and animals, obstacle detection in controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness, in general, towards increased process automation. Given the variety of conditions that may be encountered in the field, no single sensor exists that can guarantee reliable results in every scenario. The development of a multi-sensory perception system to increase the ambient awareness of an agricultural vehicle operating in crop fields is the objective of the Ambient Awareness for Autonomous Agricultural Vehicles (QUAD-AV) project. Different onboard sensor technologies, namely stereovision, LIDAR, radar, and thermography, are considered. Novel methods for their combination are proposed to automatically detect obstacles and discern traversable from non-traversable areas. Experimental results, obtained in agricultural contexts, are presented showing the effectiveness of the proposed methods.

Accurate and robust environmental perception is a critical requirement to address unsolved issues in the context of outdoor navigation, including safe interaction with living beings, obstacle detection, cooperation with other vehicles, mapping, and situation awareness in general. Aim of this paper is the development of perception algorithms to enhance the automatic understanding of the environment and develop advanced driving assistance systems for off-road vehicles. Specifically, the problem of terrain traversability assessment is addressed. Two strategies are presented. One exploits stereo data to segment drivable ground using a self-learning approach, without explicitly dealing with the obstacle detection issue, whereas the other one features a radar-stereo integrated system to detect and characterize obstacles. The paper details both methods and presents experimental results, obtained with a vehicle operating in rural and agricultural contexts.

In this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors’ coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors’ geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction. The proposed methods have been validated with synthetic and real data.

In this paper we introduce a new geometric calibration algorithm, and a geometric method of 3D reconstruction using a panoramic microwave radar and a camera. These two sensors are complementary, considering the robustness to environmental conditions and depth detection ability of the radar on one hand, and the high spatial resolution of a vision sensor on the other hand. This makes the approach well adapted for large scale outdoor cartography. Firstly, we address the global calibration problem which consists in finding the exact transformation between radar and camera coordinate systems. The method is based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required, only the knowledge of inter-targets distance is needed. This makes the method flexible and easy to use by a non expert operator. Secondly, we present a 3D reconstruction method based on sensors geometry. Both methods have been validated with synthetic and real data.

The conscience of the surrounding environment is inevitable task for several applications such as mapping, autonomous navigation and localization. In this paper we are interested by exploiting the complementarity of a panoramic microwave radar and a monocular camera for 3D reconstruction of large scale environments. Considering the robustness to environmental conditions and depth detection ability of the radar on one hand, and the high spatial resolution of a vision sensor on the other hand, makes these tow sensors well adapted for large scale outdoor cartography. Firstly, the system model of the two sensors is represented and a new 3D reconstruction method based on sensors geometry is introduced. Secondly, we address the global calibration problem which consists in finding the exact transformation between radar and camera coordinate systems. The method is based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Both methods have been validated with synthetic and real data.

In order to make efficient obstacle detection or to create a mapping, SLAM simultaneous localization and mapping being a special case, a good estimation of the sensor's move is needed. Adding dedicated extra sensors to the carrier is the most common solution but not without several limits: sensors have their own accuracy and range of use. Moreover, extra sensors are not often at the same place and so have different moves from one to another. In our outdoor application, the use of a panoramic radar as the main sensor offers another solution: the properties of the Radon's transform and the use of high resolution analysis ESPRIT allows the estimation of the motion's parameters without extra sensors, by the comparisons of successive panoramic maps. The method have been tested in both natural and urban environments and compared to motion estimated by a gyrometer and an odometer.

The vehicle-based Pelican radar system is used in the context of mobile mapping. The R-SLAM algorithm allows simultaneous retrieval of the vehicle trajectory and of the map of the environment. As the purpose of Pelican is to provide a means for gathering spatial information, the impact of distortion caused by the topography is not negligible. This article proposes an orthorectification process to correct panoramic radar images and the consequent R-SLAM trajectory and radar map. The a priori knowledge of the area topography is provided by a digital elevation model. By applying the method to the data obtained from a path with large variations in altitude it is shown that the corrected panoramic radar images are contracted by the orthorectification process. The efficiency of the orthorectification process is assessed firstly by comparing R-SLAM trajectories to a GPS trajectory and secondly by comparing the position of Ground Control Points on the radar map with their GPS position. The RMS positioning error moves from 5.56m for the raw radar map to 0.75m for the orthorectified radar map.

In a context of mobile environment mapping, a vehicle-based radar system, K2Pi, has been developed. A mapping of the environment is carried out from the radar datasets. Given the specificities of radar maps, the main problem at this stage is to find a method to georeference these maps. This article proposes three radar map georeferencing methods. The first method is a typical manual selection of a set of control point pairs. The second method consists of matching the relative trajectory computed by a specific radar algorithm with a trajectory recorded from absolute DGPS recording. Finally, the third method, inspired by the image-to-image approach, is based on Fourier-Mellin transform which automatically registers the radar map with respect to a georeferenced aerial photograph. Successfully tested on radar datasets, this method could be applied to many other types of data.

The main objective of the project IMPALA is to demonstrate that radar technology is an alternative solution to classical perception systems used in outdoor mobile robotics. This paper presents the rotating FMCW radar developed during the project and results from the combined use of radar and "Simultaneous Localization And Mapping" (SLAM) techniques in outdoor environment. Range and velocity from mobile objects can be extracted, which lead to future applications of DATMO (Detection And Tracking of Moving Objects) the first results are presented here.

L'objectif du projet IMPALA est d'evaluer l'apport du radar comme solution alternative aux moyens de perception en robotique mobile d'exterieur. Cet article illustre a travers une application de localisation et de cartographie simultanees (SLAM). les potentialites d'un radar panoramique a modulation de frequence (FMCW) qui a ete developpe au cours du projet. Donnant acces a l'information de distance et de vitesse des entites mobiles presentes dans l'environnement, le radar permet d'envisager des applications de detection et de suivi d'objets mobiles (DATMO) dont un premier resultat est presente ici.

The detection and tracking of moving objects (DATMO) in an outdoor environment from a mobile robot are difficult tasks because of the wide variety of dynamic objects. A reliable discrimination of mobile and static detections without any prior knowledge is often conditioned by a good position estimation obtained using Global Positionning System/Differential Global Positioning System (GPS/DGPS), proprioceptive sensors, inertial sensors or even the use of Simultaneous Localization and Mapping (SLAM) algorithms. In this article a solution of the DATMO problem is presented to perform this task using only a microwave radar sensor. Indeed, this sensor provides images of the environment from which Doppler information can be extracted and interpreted in order to obtain not only velocities of detected objects but also the robot's own velocity.

Soil volumetric structure is an important parameter for tillage operation. The aim of this paper is to assess whether volume characteristics can be inferred from radar measurements. A 2-D numerical model (the 2DSCAT model) was developed based on a numerical solver using a time-domain finite-element method to solve Maxwell's equations. Perfectly matched layers were implemented as well as a near- to far-field transformation. A focused incident beam was generated by adapting the boundary conditions. To represent the soil structure, a simulator was developed describing the soil as biphasic media (fine earth and clods). Clods were represented by randomly deformed ellipses, with randomly determined dimensions, locations, and orientations. The model performed successfully, as evaluated against exact analytical solutions available for an infinite perfectly conducting cylinder and the reflection of flat semi-infinite media. The model was then evaluated against measurements made by an X-band FM continuous-wave radar on a box filled with dry clods of different sizes. The effect of the clod size on the backscattering power was very well reproduced, showing the potential of using a 2-D numerical model to understand microwave-backscattering patterns from cloddy soils. Analysis of the volume scattering shows that this phenomenon can be mostly hidden in the scatter diagram by surface scattering when the latter occurred. However, the volume scattering gives a stronger residual signal in time because of propagation through the medium. Thus, time studies of the scattering signal provide further information about volume heterogeneities.