In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtaining a set of sensor measurements at each position. The goal is to process the sensor data to produce an estimate of the trajectory of the robot while concurrently building a map of the environment. In this paper, we describe a generalized framework for CML that incorporates temporal as well as spatial correlations. The representation is expanded to incorporate past vehicle positions in the state vector. Estimates of the correlations between current and previous vehicle states are explicitly maintained. This enables the consistent initialization of map features using data from multiple time steps. Updates to the map and the vehicle trajectory can also be performed in batches of data acquired from multiple vantage points. The method is illustrated with sonar data from a testing tank and via experiments with a B21 land mobile robot, demonstrating the ability to perform CML with sparse and ambiguous data. KEY WORDS—mapping, navigation, mobile robots 1.

This paper describes a system for structure-from-motion using vanishing points and three-dimensional lines extracted from omni-directional video sequences. Two novel aspects of this work are its deferred initialization of features using noisy observations from multiple, uncertain vantage points, and its use of dynamic programming for efficient 3D line tracking. We show preliminary results from the system for both indoor and outdoor sequences.

In this paper, we present a vision-based terrain mapping and analysis system, and a model predictive control (MPC)based flight control system, for autonomous landing of a helicopter-based unmanned aerial vehicle (UAV) in unknown terrain. The vision system is centered around Geyer et al.’s Recursive Multi-Frame Planar Parallax algorithm [1], which accurately estimates 3D structure using georeferenced images from a single camera, as well as a modular and efficient mapping and terrain analysis module. The vision system determines the best trajectory to cover large areas of terrain or to perform closer inspection of potential landing sites, and the flight control system guides the vehicle through the requested flight pattern by tracking the reference trajectory as computed by a real-time MPC-based optimization. This trajectory layer, which uses a constrained system model, provides an abstraction between the vision system and the vehicle. Both vision and flight control results are given from flight tests with an electric UAV. 1

This chapter adch-esses an open problem in visual motion analysis, the estimation of image motion in the vicinity of occlusion boundaries. With a Bayesian formulation, local image motion is explained in terms of multiple, competing, nonlinear models, including models for smooth (translational) motion and for motion boundaries. The generarive model for motion boundaries explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We formulate the posterior probability distribution over the models and model parameters, conditioned on the image sequence. Approximate inference is achieved with a combination of tools: A Bayesian filter provides for online computation; factored sampling allows us to represent multimodal nonGaussian distributions and to propagate beliefs with nonlinear dynamics from one time to the next; and mixture models are used to simplify the computation of joint prediction distributions in the Bayesian filter. To efficiently represent such a high-dimensional space we also initialize samples using the responses of a lowlevel motion discontinuity detector. The basic formulation and computational model provide a general probabilistic framework for motion estimation with multiple, non-linear, models.

Standard structure from motion algorithms recover 3D structure of points. If a surface representation is desired, for example a piece-wise planar representation, then a two-step procedure typically follows: in the first step the plane-membership of points is first determined manually, and in a subsequent step planes are fitted to the sets of points thus determined, and their parameters are recovered. This paper presents an approach for automatically segmenting planar structures from a sequence of images, and simultaneously estimating their parameters. In the proposed approach the plane-membership of points is determined automatically, and the planar structure parameters are recovered directly in the algorithm rather than indirectly in a post-processing stage. Simulated and real experimental results show the efficacy of this approach.

We present a system that consists of one camera connected to a Personal Computer that can (a) select and track a number of high-contrast point features on a sequence of images, (b) estimate their three-dimensional motion and position relative to an inertial reference frame, assuming rigidity, (c) handle occlusions that cause pointfeatures to disappear as well as new features to appear. The system can also (d) perform partial self-calibration and (e) check for consistency of the rigidity assumption, although these features are not implemented in the current release. All of this is done automatically and in realtime (30Hz) for 40-50 point-features using commercial offthe-shelf hardware. The system is based on an algorithm presented by Chiuso et al. [3] whose properties have been analyzed in [2]. In particular, the algorithm is provably observable, provably minimal and provably stable- under suitable conditions. The core of the system, consisting of C++ code ready to interface with a frame grabber as well as Matlab code for development, is available at

Complete digital recording of Cultural Heritage is a multidimensional process. It depends highly on the nature of the subject of recording as well as the purpose of its recording. The whole process involves the three-dimensional digitization, digital data processing and storage, archival and management, representation and reproduction. In this paper we briefly review methods for three-dimensional digitization that are applicable to cultural heritage recording.

While the geometric aspects of structure and motion estimation from uncalibrated images are well understood, and it has great promise in applications, it has not seen widespread use. In this paper we combine SSD tracking with incremental structure computation into a system computing both motion and structure on-line from video. We show how in combination the structure estimation and tracking benefit each other, resulting in both better structure and more robust tracking. Particularly, through the 3D structure, our method can manage visibility constraints, add new image patches to track as they come into view and remove ones that are occluded or fail. This allows tracking over larger pose variations than possible with conventional SSD tracking (e.g. going around an object or scene where new parts come into view.) Experiments demonstrate tracking and capture of a scene from a camera trajectory covering different sides without mutual visibility.

Methods for reconstructing photorealistic 3D graphics models from images or video are appealing applications of computer vision. Such methods rely on good input image data, but the lack of user feedback during image acquisition often leads to incomplete or poorly sampled reconstruction results. We describe a video-based system that constructs and visualizes a coarse graphics model in real-time and automatically saves a set of images appropriate for later offline dense reconstruction. Visualization of the model during image acquisition allows the operator to interactively verify that an adequate set of input images has been collected for the modeling task, while automatic image selection keeps storage requirements to a minimum. Our implementation uses real-time monocular SLAM to compute and continuously keep extending a 3D model, augments this with keyframe selection for storage, surface modelling, and on-line rendering of the current structure textured from a selection of key-frames. This rendering gives an immediate and intuitive view of both the geometry and if suitable viewpoints of texture images have already been captured. 1.

this memory overhead could be overwhelming. For example, on a dual SMP system with 256 megabytes of physical memory, the number of ppage objects is well over 60,000. If each ppage object uses a scalable spinlock, the amount of memory overhead reaches 32 megabytes. Using the trivial implementation, each spinlock resides on a cache line, yielding a small memory overhead of less than 2 megabytes. The second benefit concerns latency. The trivial algorithm has lower lock acquire and release latencies because it does not need to perform enqueue and dequeue operations. Since latency is a concern with fine grained locking, the trivial algorithm becomes attractive