The Journal of the Acoustical Society of America, 2013
A method is presented in which a (large) swarm of sensor motes perform simple ultrasonic ranging ... more A method is presented in which a (large) swarm of sensor motes perform simple ultrasonic ranging measurements. The method allows to localize the motes within the swarm, and at the same time, map the environment which the swarm has traversed. The motes float passively uncontrolled through the environment and do not need any other sensor information or external reference other than a start and end point. Once the motes are retrieved, the stored data can be converted into the motes relative positions and a map describing the geometry of the environment. This method provides the possibility to map inaccessible or unknown environments where electro-magnetic signals, such as GPS or radio, cannot be used and where placing beacon points is very hard. An example is underground piping systems transporting liquids. Size and energy constraints together with the occurrence of reverberations pose challenges in the way the motes perform their measurements and collect their data. A minimalistic approach in the use of ultrasound is pursued, using an orthogonal frequency division multiplexing technique for the identification of motes. Simulations and scaled air-coupled 45-65 kHz experimental measurements have been performed and show feasibility of the concept.
17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014
This work concentrates on vision processing for ADAS and intelligent vehicle applications. We pro... more This work concentrates on vision processing for ADAS and intelligent vehicle applications. We propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. Our extension learns color appearance models for road and obstacle classes in an online and self-supervised fashion. The algorithm is tightly integrated within the core of the optimization process of the original Stixel World, allowing for strong fusion of the disparity and color signals. We perform an extensive evaluation, including different self-supervised learning strategies and different color models. Our newly recorded, publicly available data set is intentionally focused on challenging traffic scenes with many low-texture regions, causing numerous disparity artifacts. In this evaluation, we increase the F-score of the drivable distance from 0.86 to 0.97, compared to a tuned version of the stateof-the-art baseline method. This clearly shows that our color extension increases the robustness of the Stixel World, by reducing the number of falsely detected obstacles while not deteriorating the detection of true obstacles.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012
Empirical evidence shows that error growth in visual odometry is biased. A projective bias model ... more Empirical evidence shows that error growth in visual odometry is biased. A projective bias model is developed and its parameters are estimated offline from trajectories encompassing loops. The model is used online to compensate for bias and thereby significantly reduces error growth. We validate our approach with more than 25 km of stereo data collected in two very different urban environments from a moving vehicle. Our results demonstrate significant reduction in error, typically on the order of 50%, suggesting that our technique has significant applicability to deployed robot systems in GPS denied environments.
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008
A novel robust visual-odometry technique, called EM-SE(3) is presented and compared against using... more A novel robust visual-odometry technique, called EM-SE(3) is presented and compared against using the random sample consensus (RANSAC) for ego-motion estimation. In this contribution, stereo-vision is used to generate a number of minimal-set motion hypothesis. By using EM-SE(3), which involves expectation maximization on a local linearization of the rigid-body motion group SE(3), a distinction can be made between inlier and outlier motion hypothesis. At the same time a robust mean motion as well as its associated uncertainty can be computed on the selected inlier motion hypothesis. The datasets used for evaluation consist of synthetic and large real-world urban scenes, including several independently moving objects. Using these data-sets, it will be shown that EM-SE is both more accurate and more efficient than RANSAC.
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
In robotic applications the absolute pose is often obtained as the integral of successive relativ... more In robotic applications the absolute pose is often obtained as the integral of successive relative rigid-body motions. As each relative rigid-body motion is typically the product of statistical inference, the integrated absolute pose will exhibit error build-up and the estimated trajectory will differ from the true trajectory undertaken by the system. Some application areas allow the system to receive additional information about its current absolute pose, for example from loop detection, which is more accurate than the integral of the relative rigid-body motions. The availability of this absolute information is usually less frequent than the information underlying the relative rigidbody motions. This contribution addresses an efficient closed form algorithm which minimally bends a trajectory such that the integrated pose is exactly equal to any particular desired pose. The manner in which the bending is distributed over the trajectory is controllable using weights. The proposed method will be compared against a maximum likelihood solution on simulated trajectories as well as on trajectories estimated from binocular and monocular data. The results indicate that the performance differences between the closed form approach and the maximum likelihood solution are negligible while the closed form approach is significantly more efficient.
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
This contribution addresses the problem of bias in stereo based motion estimation. Using a biased... more This contribution addresses the problem of bias in stereo based motion estimation. Using a biased estimator within a visual-odometry system will cause significant drift on large trajectories. This drift is often minimized by exploiting auxiliary sensors, (semi-)global optimization or loop-closing. In this paper it is shown that bias in the motion estimates can be caused by incorrect modeling of the uncertainties in landmark locations. Furthermore, there exists a relation between the bias, the true motion and the distribution of landmarks in space. Guided by these observations, a novel bias reduction technique has been developed. The core of the proposed method is computing the difference between motion estimates obtained using dissimilar heteroscedastic landmark uncertainty models. This approach is accurate, efficient and does not rely on auxiliary sensors, (semi-)global optimization or loop-closing. To show the real-world applicability of the proposed method, it has been tested on several data-sets including a challenging 5 km urban trajectory. The gain in performance is clearly noticeable.
Riemannian geometry allows for the generalization of statistics designed for Euclidean vector spa... more Riemannian geometry allows for the generalization of statistics designed for Euclidean vector spaces to Riemannian manifolds. It has recently gained popularity within computer vision as many relevant parameter spaces have such a Riemannian manifold structure. Approaches which exploit this have been shown to exhibit improved efficiency and accuracy. The Riemannian logarithmic and exponential mappings are at the core of these approaches. In this contribution we review recently proposed Riemannian mappings for essential matrices and prove that they lead to sub-optimal manifold statistics. We introduce correct Riemannian mappings by utilizing a multiple-geodesic approach and show experimentally that they provide optimal statistics.
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
This work provides a feasibility study on estimating the 3-D locations of several thousand miniat... more This work provides a feasibility study on estimating the 3-D locations of several thousand miniaturized freefloating sensor platforms. The localization is performed on basis of sparse ultrasound range measurements between sensor platforms and without the use of beacons.
In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out... more In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out-of-core solution for loop-closing exactly sparse trajectories. Here we extend it to fuse exactly sparse trajectories, obtained from relative pose estimates, with absolute orientation data. This allows us to close-the-loop using absolute orientation data only. The benefit is that our approach does not rely on the observations from which the trajectory was estimated nor on the probabilistic links between poses in the trajectory. It therefore is highly efficient. The proposed method is compared against regular fusion and an iterative trajectory bending solution using a 5 km long urban trajectory. Proofs concerning optimality of our method are provided.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
We have developed a stereo vision based obstacle detection (OD) system that can be used to detect... more We have developed a stereo vision based obstacle detection (OD) system that can be used to detect obstacles in off-road terrain during both day and night conditions. In order to acquire enough depth estimates for reliable OD during low visibility conditions, we propose a stereo disparity (depth) estimation approach that uses fine-to-coarse selection in a stereo image pyramid. This fine-to-coarse selection is based on a novel disparity validity metric that reflects the estimation reliability. Dense three-dimensional terrain data is reconstructed from the estimated stereo disparities. In our OD methods, several geometric properties, such as the terrain slope, are inspected to distinguish between obstacles and drivable terrain. This is achieved in a robust and efficient manner by considering the inherent uncertainty in stereo depth and using a hysteresis threshold. A large and varied collection of day-and nighttime images has been used to evaluate the performance of our system. The results show that our methods can reliably detect different types of obstacles in all tested conditions.
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Papers by Gijs Dubbelman