Werner Goeman

Evaluating corner detectors for the extraction of man-made structures in urban areas

We analyze if the presence of corners in very high resolution (VHR) satellite images can give us an indication on the type of structure present in a scene (man-made versus natural structures). Two the corner detectors are validated in this respect: Harris and SUSAN. The detection performance is evaluated over a spectrum of spatial resolutions for current and future VHR systems (from 2 meters to 17 centimeters). The ground truth of this study consists of annotated image extracts containing different types of man-made structures, in which the relevant corners have been identified.

Towards online mobile mapping using inhomogeneous lidar data

In this paper we present a novel approach to quickly obtain detailed 3D reconstructions of large scale environments. The method is based on the consecutive registration of 3D point clouds generated by modern lidar scanners such as the Velodyne HDL-32e or HDL-64e. The main contribution of this work is that the proposed system specifically deals with the problem of sparsity and inhomogeneity of the point clouds typically produced by these scanners. More specifically, we combine the simplicity of the traditional iterative closest point (ICP) algorithm with the analysis of the underlying surface of each point in a local neighbourhood. The algorithm was evaluated on our own collected dataset captured with accurate ground truth. The experiments demonstrate that the system is producing highly detailed 3D maps at the speed of 10 sensor frames per second.

Characterizing the performance of automatic road detection using error propagation

Abstract A methodology is introduced to predict the performance of automatic road detection using image examples of typical road types. In contrast to previous work on road detection, the focus is on characterizing the detection performance to achieve reliable performance measures of the detection. It is studied how noise, like road markings, shadows, trees and buildings, influences the detection of road. This noise is modeled using second-order statistics and its effects are calculated using error propagation on the detection equations. The method predicts the performance in terms of detection rate and gives the optimal parameter set that is needed for this detection. Experiments have been conducted on a set of images of typical roads in very high-resolution satellite images.

3D Scene Reconstruction Using Omnidirectional Vision and LiDAR: A Hybrid Approach.

In this paper, we propose a novel approach to obtain accurate 3D reconstructions of large-scale environments by means of a mobile acquisition platform. The system incorporates a Velodyne LiDAR scanner, as well as a Point Grey Ladybug panoramic camera system. It was designed with genericity in mind, and hence, it does not make any assumption about the scene or about the sensor set-up. The main novelty of this work is that the proposed LiDAR mapping approach deals explicitly with the inhomogeneous density of point clouds produced by LiDAR scanners. To this end, we keep track of a global 3D map of the environment, which is continuously improved and refined by means of a surface reconstruction technique. Moreover, we perform surface analysis on consecutive generated point clouds in order to assure a perfect alignment with the global 3D map. In order to cope with drift, the system incorporates loop closure by determining the pose error and propagating it back in the pose graph. Our algorithm was exhaustively tested on data captured at a conference building, a university campus and an industrial site of a chemical company. Experiments demonstrate that it is capable of generating highly accurate 3D maps in very challenging environments. We can state that the average distance of corresponding point pairs between the ground truth and estimated point cloud approximates one centimeter for an area covering approximately 4000 m2. To prove the genericity of the system, it was tested on the well-known Kitti vision benchmark. The results show that our approach competes with state of the art methods without making any additional assumptions.

Automatic and robust extrinsic camera calibration for high-accuracy mobile mapping

A mobile mapping system (MMS) is the answer of the geoinformation community to the exponentially growing demand for various geospatial data with increasingly higher accuracies and captured by multiple sensors. As the mobile mapping technology is pushed to explore its use for various applications on water, rail, or road, the need emerges to have an external sensor calibration procedure which is portable, fast and easy to perform. This way, sensors can be mounted and demounted depending on the application requirements without the need for time consuming calibration procedures. A new methodology is presented to provide a high quality external calibration of cameras which is automatic, robust and fool proof.The MMS uses an Applanix POSLV420, which is a tightly coupled GPS/INS positioning system. The cameras used are Point Grey color video cameras synchronized with the GPS/INS system. The method uses a portable, standard ranging pole which needs to be positioned on a known ground control point. For calibration a well studied absolute orientation problem needs to be solved. Here, a mutual information based image registration technique is studied for automatic alignment of the ranging pole. Finally, a few benchmarking tests are done under various lighting conditions which proves the methodology’s robustness, by showing high absolute stereo measurement accuracies of a few centimeters.

Robust statistics for automated quality assessment of road network data based on VHR images

A method is explored to assess the quality of road network data based on image information in a reliable and accurate way. In the field of geography, an accuracy assessment method, called buffer-overlay-statistics, is known to assess the spatial quality of a line data set by using another line data set of higher spatial accuracy. Here, the method is adapted to assess the quality of a line data set based on image information rather than vector data. The average displacement accuracy measure is redefined, such that it is able to take into account line detection errors (fragmentation and noise). Experiments are conducted on artificial data showing how road extraction out of very high resolution satellite images can be used to asses the spatial accuracy of an existing road vector database.

Consistent ICP for the registration of sparse and inhomogeneous point clouds

In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms.

Automatic camera to laser calibration for high accuracy mobile mapping systems using INS

A mobile mapping system (MMS) is a mobile multi-sensor platform developed by the geoinformation community to support the acquisition of huge amounts of geodata in the form of georeferenced high resolution images and dense laser clouds. Since data fusion and data integration techniques are increasingly able to combine the complementary strengths of different sensor types, the external calibration of a camera to a laser scanner is a common pre-requisite on todays mobile platforms. The methods of calibration, nevertheless, are often relatively poorly documented, are almost always time-consuming, demand expert knowledge and often require a carefully constructed calibration environment. A new methodology is studied and explored to provide a high quality external calibration for a pinhole camera to a laser scanner which is automatic, easy to perform, robust and foolproof. The method presented here, uses a portable, standard ranging pole which needs to be positioned on a known ground control point. For calibration, a well studied absolute orientation problem needs to be solved. In many cases, the camera and laser sensor are calibrated in relation to the INS system. Therefore, the transformation from camera to laser contains the cumulated error of each sensor in relation to the INS. Here, the calibration of the camera is performed in relation to the laser frame using the time synchronization between the sensors for data association. In this study, the use of the inertial relative movement will be explored to collect more useful calibration data. This results in a better intersensor calibration allowing better coloring of the clouds and a more accurate depth mask for images, especially on the edges of objects in the scene.

On the design of reliable graph matching techniques for change detection

In this paper, we use inexact graph matching to detect changes between spatial features coming from different data sources, e.g. image derived information versus a GIS layer. Corresponding features in the data sources need to be matched taking into account outliers and spatial inaccuracy. We discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. A condition based on the expected graph error is presented which allows to determine the bounds of error tolerance and in this way characterizes acceptable over inacceptable data inconsistencies.

Defining consistency to detect change using inexact graph matching

In this paper, we discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. Consistency allows us to study the cost function which controls the graph matching process, regardless of the optimization technique that is used. The analysis is performed in the context of change detection in geospatial information. A condition based on the expected graph error is presented which allows to determine the bounds of error tolerance and in this way characterizes acceptable over inacceptable data inconsistencies.