Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos

Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsic parameters corresponding to such cameras are often unknown or change over time. Typical manual calibration approaches require objects such as a chessboard or additional scene-specific information. On the other hand, automatic camera calibration does not have such requirements. Yet, the automatic calibration of dashboard cameras is challenging as forward and planar navigation results in critical motion sequences with reconstruction ambiguities. Structure reconstruction of complete visual-sequences that may contain tens of thousands of images is also computationally untenable. Here, we propose a system for practical monocular onboard camera auto-calibration from crowdsourced videos. We show the effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate its application for accurate monocular dense depth and ego-motion estimation on uncalibrated videos.Comment: Accepted at 16th International Conference on Computer Vision Theory and Applications (VISAP, 2021

Method for orthorectification of terrestrial radar maps

International audienceThe 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 5.56 m for the raw radar map to 0.75 m for the orthorectified radar map

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

The hyperspectral cameras use has been increasing over the past years, driven by the exponential growth of the computational systems power. The capability of acquiring multiple spectre wavelengths benefits the increase of the hyperspectral systems range of applications. However, until now, most hyperspectral systems are used in posprocessing and do not allow to take full advantage of the system capabilities. There is a recent trend to be able to use hyperspectral systems in real-time. Given the recent problems in European Union borders due to irregular immigration and drug smuggling, there is the need to develop novel autonomous surveillance systems that can work on these scenarios. This thesis addresses the scenario of using hyperspectral imaging systems for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. In our work, we develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop a novel method of distinguish targets (boats) from their dominant background. With this application our system is able to only select relevant information to send to a remote station on the ground, thus making it suitable to be installed in an actual unmanned maritime surveillance system.A utilização de câmaras hiperespectrais tem vindo a aumentar nos últimos anos, motivada pelo crescimento exponencial da capacidade de processamento dos mais recentes sistemas computacionais. A sua aptidão para observar múltiplos comprimentos de onda beneficia aplicações em diferentes campos de atividade. No entanto, a maior parte das aplicações com câmaras hiperespectrais são realizadas em pós-processamento, não aproveitando totalmente as capacidades destes sistemas. Existe uma necessidade emergente de detetar mais características sobre o cenário que está a ser observado, incentivando o desenvolvimento de sistemas hiperespectrais capazes de adquirir e processar informação em tempo-real. Face aos mais recentes problemas de emigração e contrabando ilegal na União Europeia, surge a necessidade da realização de vigilância autónoma capaz de adquirir o máximo de informação possível sobre os meios envolventes presentes num dado percurso. E neste contexto que se insere a dissertação que visa a criação é implementação de um sistema hiperespectral em tempo-real. Para construir o sistema, foi necessário dividir o problema em diferentes etapas. Iniciou-se por um estudo detalhado dos sistemas hiperespectrais, desenvolvendo um método de calibração dos ângulos de boresight, que permitiu calibrar a relação entre o sistema de posicionamento e navegação da câmara hiperespectral e o sensor imagem. Esta calibração, permite numa fase posterior geo-referenciar os alvos com maior precisão. Posteriormente, foi criada uma pipeline de processamento, que permite analisar os espectros obtidos, distinguindo os alvos do cenário onde estão inseridos. Após a deteção dos alvos, procede-se `a sua geo-referenciação, de forma a obter as coordenadas UTM do alvo. Toda a informação obtida sobre o alvo e a sua posição é enviada para uma estacão em terra, de forma a ser validada por um humano. Para tal, foi também desenvolvida a metodologia de envio, para selecionar a informação a enviar apenas à mais relevante

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods