Navigation and Mapping for Aerial Vehicles Based on Inertial and Imaging Sensors

Abstract

Small and medium sized Unmanned Aerial Vehicles (UAV) are today used in military missions, and will in the future find many new application areas such as surveillance for exploration and security. To enable all these foreseen applications, the UAV's have to be cheap and of low weight, which restrict the sensors that can be used for navigation and surveillance. This thesis investigates several aspects of how fusion of navigation and imaging sensors can improve both tasks at a level that would require much more expensive sensors with the traditional approach of separating the navigation system from the applications. The core idea is that vision sensors can support the navigation system by providing odometric information of the motion, while the navigation system can support the vision algorithms, used to map the surrounding environment, to be more efficient. The unified framework for this kind of approach is called Simultaneous Localisation and Mapping (SLAM) and it will be applied here to inertial sensors, radar and optical camera.Synthetic Aperture Radar (SAR) uses a radar and the motion of the UAV to provide an image of the microwave reflectivity of the ground. SAR images are a good complement to optical images, giving an all-weather surveillance capability, but they require an accurate navigation system to be focused which is not the case with typical UAV sensors. However, by using the inertial sensors, measuring UAV's motion, and information from the SAR images, measuring how image quality depends on the UAV's motion, both higher navigation accuracy and, consequently, more focused images can be obtained. The fusion of these sensors can be performed in both batch and sequential form. For the first approach, we propose an optimisation formulation of the navigation and focusing problem while the second one results in a filtering approach. For the optimisation method the measurement of the focus in processed SAR images is performed with the image entropy and with an image matching approach, where SAR images are matched to the map of the area. In the proposed filtering method the motion information is estimated from the raw radar data and it corresponds to the time derivative of the range between UAV and the imaged scene, which can be related to the motion of the UAV.Another imaging sensor that has been exploited in this framework is an ordinary optical camera. Similar to the SAR case, camera images and inertial sensors can also be used to support the navigation estimate and simultaneously build a three-dimensional map of the observed environment, so called inertial/visual SLAM. Also here, the problem is posed in optimisation framework leading to batch Maximum Likelihood (ML) estimate of the navigation parameters and the map. The ML problem is solved in both the straight-forward way, resulting in nonlinear least squares where both map and navigation parameters are considered as parameters, and with the Expectation-Maximisation (EM) approach. In the EM approach, all unknown variables are split into two sets...

Two‐dimensional autofocus technique for high‐resolution spotlight synthetic aperture radar

“…The irregular motion of the synthetic aperture radar (SAR) platform caused by atmosphere turbulence as well as its own vibration yields substantial envelop migrations into other range cells and complicated phase aberrations, which considerably degrades SAR focus [1][2][3]. Such errors can be partly cancelled by a navigation system, but for very high-resolution data, broadening of the SAR impulse response function deteriorates the resolution, and autofocus algorithms must be developed to estimate residual errors beyond the capacity of the navigation system.…”

Collaborative spatial reasoning for environment mapping using unmanned aerial vehicles

“…To contribute to the evolution of existing navigation systems, this project is focused on the improvement of a system applied to Unmanned Aerial Vehicles (UAVs). Currently, UAVs are being intensively used for military protection purposes, recognizing battlefields or the location of the opposing army (SJANIC, 2013), but there is great potential for UAV applications for civil use, by the police, firefighters, environmental activities such as fire tracking, damage survey after catastrophes such as earthquakes, tsunamis, among others, also considering the automatic pilots of commercial aircraft, already widely used and which can also benefit from the evolution of the system. Figure 1 shows some examples of using UAVs in the civil sector.…”

“…When multiple agents are observing a scene and occlusion prevents sensors from assessing parts of the objects present in the scene, distinct observers can provide multiple viewpoint descriptions Furthermore, there is a problem for using external systems to aid navigation systems. The GNSS (Global Navigation Satellite System) can contribute significantly to improve the accuracy of UAVs auto-locating and assistance in mapping the environment, but signal availability can be affected by occlusions or by the intentional blocking of the signal, in case of more hostile environments (SJANIC, 2013). The intelligent formalism developed in this work for environment mapping from partial aerial point of view using sensors that can capture signals to scene interpretation, could turn the agents less dependent of external systems.…”