Abstract. In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle’s motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer. Keywords: laser scanning, navigation, self-localization, mobile robots, 3D modeling, Monte-Carlo localization 1.

Abstract. In this paper, the structure from motion (SfM) problem is addressed using sequen-tial Monte Carlo methods. A new SfM algorithm based on random sampling is derived to estimate the posterior distributions of camera motion and scene structure for the perspective projection camera model. Experimental results show that challenging issues in solving the SfM problem, due to erroneous feature tracking, feature occlusion, motion/structure ambigu-ity, mixed-domain sequences, mismatched features, and independently moving objects, can be

We describe a linear-time algorithm that recovers absolute camera orientations and positions, along with uncertainty estimates, for networks of terrestrial image nodes spanning hundreds of meters in outdoor urban scenes. The algorithm produces pose estimates globally consistent to roughly 0.1 # (2 milliradians) and 5 centimeters on average, or about four pixels of epipolar alignment.

An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a “virtual camera ” is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a consequence, the calibration matrices (intrinsics) of the virtual cameras can be considered completely known, and their poses are known up to a single angular displacement parameter. Given pose hypotheses obtained in the multiple virtual camera views, the recovery of 3D body pose and camera relative orientations is formulated as a stochastic optimization problem. An Expectation-Maximization algorithm is derived that can obtain the locally most likely (self-consistent) combination of body pose hypotheses. Performance of the approach is evaluated with synthetic sequences as well as real video sequences of human motion. 1.

. A standard method for handling Bayesian models is to use Markov chain Monte Carlo methods to draw samples from the posterior. We demonstrate this method on two core problems in computer vision---structure from motion and colour constancy. These examples illustrate a samplers producing useful representations for very large problems. We demonstrate that the sampled representations are trustworthy, using consistency checks in the experimental design. The sampling solution to structure from motion is strictly better than the factorisation approach, because: it reports uncertainty on structure and position measurements in a direct way; it can identify tracking errors; and its estimates of covariance in marginal point position are reliable. Our colour constancy solution is strictly better than competing approaches, because: it reports uncertainty on surface colour and illuminant measurements in a direct way; it incorporates all available constraints on surface reflectance and on illumination in a direct way; and it integrates a spatial model of reflectance and illumination distribution with a rendering model in a natural way. One advantage of a sampled representation is that it can be resampled to take into account other information. We demonstrate the effect of knowing that, in our colour constancy example, a surface viewed in two different images is in fact the same object. We conclude with a general discussion of the strengths and weaknesses of the sampling paradigm as a tool for computer vision. Keywords: Markov chain Monte Carlo, colour constancy, structure from motion 1.

Abslracl-I1 is essential in many applications that mobde robots localize themselves with respect to an unknown environmenL This means that the robot must build a map of its environment and then localize using the map. This pmeess is called simultaneous Iodization and mapping (SLAM). This paper presents an iterative solution to the landmark initialization problem inherent in a bearing-only implementation of SLAM. No prior knowledge of the environment is required, and furthermore, there (up no requirements ahout having Qe data association problem solved. Once landmarks are initialized, they are inserted into an extended Kalman Filter (Em) lo solve the SLAM problem. Both indoor and outdoor experiments are presented to validate the method. I.

Abstract- This paper presents a new algorithm for determining the trajectory of a mobile robot and, simultaneously, creating a detailed volumetric 3D model of its workspace. The algorithm exclusively utilizes information provided by a single stereo vision system, avoiding thus the use both of more costly laser systems and error-prone odometry. Six-degrees-of-freedom egomotion is directly estimated from images acquired at relatively close positions along the robot’s path. Thus, the algorithm can deal with both planar and uneven terrain in a natural way, without requiring extra processing stages or additional orientation sensors. The 3D model is based on an octree that encapsulates clouds of 3D points obtained through stereo vision, which are integrated after each egomotion stage. Every point has three spatial coordinates referred to a single frame, as well as true-color components. The spatial location of those points is continuously improved as new images are acquired and integrated into the model. Index Terms- 3D SLAM. Mobile robots. Stereo vision. I.

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When trying to recover 3D structure from a set of images, the most difficult problem is establishing the correspondence between the measurements. Most existing approaches assume that features can be tracked across frames, whereas methods that exploit rigidity constraints to facilitate matching do so only under restricted camera motion. In this paper we propose a Bayesian approach that avoids the brittleness associated with singling out one "best" correspondence, and instead consider the distribution over all possible correspondences. We treat both a fully Bayesian approach that yields a posterior distribution, and a MAP approach that makes use of EM to maximize this posterior. We show how Markov chain Monte Carlo methods can be used to implement these techniques in practice, and present experimental results on real data. 1 Introduction Structure from motion (SFM) addresses the problem of simultaneously recovering camera pose and a three-dimensional model from a collection...

The problem of "Structure From Motion" is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of the points. Under simplified camera models, the problem reduces to factorizing a measurement matrix into the product of two low rank matrices.