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26:21 FOSS4G, Open Source Geospatial Foundation (OSGeo) English 2015

An On-board Visual-based Attitude Estimation System For Unmanned Aerial Vehicle Mapping

A visual-based attitude estimation system aims to utilize an on-board camera to estimate the pose of the platform by using salient image features rather than additional hardware such as gyroscope. One of the notable achievements in this approach is on-camera self-calibration [1-4] which has been widely used in the modern digital cameras. Attitude/pose information is one of the crucial requirements for the transformation of 2-dimensional (2D) image coordinates to 3-dimensional (3D) real-world coordinates [3]. In photogrammetry and machine vision, the use of camera’s pose is essential for modeling tasks such as photo modeling [5-8] and 3D mapping [9]. Commercial software packages are now available for such tasks, however, they are only good for off-board image processing which does not have any computing or processing constraints. Unmanned Aerial Vehicles (UAVs) and any other airborne platforms impose several constraints to attitude estimation. Currently, Inertial Measurement Units (IMUs) are widely used in unmanned aircrafts. Although IMUs are very effective, this conventional attitude estimation approach adds up the aircraft’s payload significantly [10]. Hence, a visual-based attitude estimation system is more appropriate for UAV mapping. Different types of approaches to visual-based attitude estimation have been proposed in [10-14]. This study aims to integrate optical flow and a keypoints detector of overlapped images for on-board attitude estimation and camera-self calibration. This is to minimize the computation burden that can be caused by the optical flow, and to fit in on-board visual-based attitude estimation and camera calibration. A series of performance tests have been conducted on selected keypoints detectors, and the results are evaluated to identify the best detector for the proposed visual-based attitude estimation system. The proposed on-board visual-based attitude estimation system is designed to use visual information from overlapped images to measure the platform’s egomotion, and estimate the attitude from the visual motion. Optical flow computation could be expensive depending on the approach [15]. Our goal is to reduce the computation burden at the start of the processing by minimizing the aerial images to the regions of upmost important. This requires an integration of optical flow with salient feature detection and matching. Our proposed system strictly follows the UAV’s on-board processing requirements [16]. Thus, the suitability of salient feature detectors for the system needs to be investigated. Performances of various keypoints detectors have been evaluated in terms of detection, time to complete and matching capabilities. A set of 249 aerial images acquired from a fixed wing UAV have been tested. The test results show that the best keypoints detector to be integrated in our proposed system is the Speeded Up Robust Feature (SURF) detector, given that Sum of Absolute Differences (SAD) matching metric is used to identify the matching points. It was found that the time taken for SURF to complete the detection and matching process is, although not the fastest, relatively small. SURF is also able to provide sufficient numbers of salient feature points in each detection without sacrificing the computation time.
  • Published: 2015
  • Publisher: FOSS4G, Open Source Geospatial Foundation (OSGeo)
  • Language: English
31:14 FOSS4G, Open Source Geospatial Foundation (OSGeo) English 2015

Object-Based Building Boundary Extraction From Lidar Data

Urban areas are of increasing importance in most of the countries since they have been changing rapidly over time. Buildings are the main objects of these areas, and building boundaries are one of the key factors for urban mapping and city modelling. Accurate building extraction using lidar data has been a prevalent topic that many research efforts have been contributed to. However, the complexity of building shapes and irregularity of lidar point distribution make the task difficult to achieve. Although there are plenty of algorithms trying to solve the difficulties, it is not feasible for a single method to fit for all. Each can perform well under a certain situation and requirement only. In this paper, several building boundary extraction algorithms including an alpha-shape algorithm, a grid-based algorithm, and a concave hull algorithm are assessed. The strengths and limitations of each algorithm are identified and addressed. The point cloud used in this research is derived from the airborne lidar data acquired over the main campus of the University of New South Wales (UNSW) Australia in 2005. Typically, the boundary extraction algorithms are applied to the clusters of building points when lidar data is segmented and classified. Many approaches have been attempted to improve the extraction algorithms. The simplest way to extract a rough boundary is using the convex hull method which has been implemented by several researchers including Qihong et al. [1]. However, this algorithm only fits for buildings with regular convex shapes. In order to overcome the limitation of this method many researchers have modified and improved the algorithm and obtained more reliable boundaries [2, 3]. Another prevalent and recent method is using an alpha-shape algorithm based on two-dimensional Delaunay Triangulation [4, 5]. This method works for both concave and convex shapes, and even for some complicated shapes. Another approximation-based algorithm was introduced by Zhou and Neumann [6] using watertight grids. Although it is observed that aforementioned algorithms work well in different scenarios, a quantitative comparison analysis on each algorithm��s performance on an identical dataset is rarely reported. Aiming at evaluating and improving these algorithms, we implemented a mathematical framework to compare the algorithms in an object-by-object basis. This study compares the boundary points selected by different algorithms and the impact of the selection on the accuracy. In this paper, three algorithms for building boundary extraction are assessed in an object-by-object basis. The alpha-shape algorithm generates reliable boundaries for most of sample buildings, while the grid-based algorithm shows a little inconsistency in some cases. The concave hull algorithm performs moderately with a few limitations. The alpha-shape algorithm is suggested for general building boundary extraction for its consistency and reliability.
  • Published: 2015
  • Publisher: FOSS4G, Open Source Geospatial Foundation (OSGeo)
  • Language: English
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