Mario E. Munich

The vSLAM Algorithm for Robust Localization and Mapping

This paper presents the Visual Simultaneous Localization and Mapping (vSLAMTM) algorithm, a novel algorithm for simultaneous localization and mapping (SLAM). The algorithm is vision-and odometry-based, and enables low-cost navigation in cluttered and populated environments. No initial map is required, and it satisfactorily handles dynamic changes in the environment, for example, lighting changes, moving objects and/or people. Typically, vSLAM recovers quickly from dramatic disturbances, such as “kidnapping”.

Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification

The problem of establishing correspondence and measuring the similarity of a pair of planar curves arises in many applications in computer vision and pattern recognition. This paper presents a new method for comparing planar curves and for performing matching at sub-sampling resolution. The analysis of the algorithm as well as its structural properties are described. The performance of the new technique applied to the problem of signature verification is shown and compared with the performance of the well-known Dynamic Time Warping algorithm.

Visual identification by signature tracking

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics.

Optical sensing for robot perception and localization

Optical sensing, e.g., computer vision, provides a very compelling approach to solving a number of technological challenges for developing affordable, useful, and reliable robotic products. We describe key advancements in the field consisting of three core technologies for visual pattern recognition (ViPR), visual simultaneous localization and mapping (vSLAM), and a low-cost solution for localization using optical beacons (NorthStar). ViPR is an algorithm for visual pattern recognition based on scale invariant features (SIFT features) which provides a robust and computationally effective solution to fundamental vision problems including the correspondence problem; object recognition; structure; and pose estimation. vSLAM is an algorithm for visual simultaneous localization and mapping using one camera sensor in conjunction with dead-reckoning information, e.g., odometry. vSLAM provides a cost-effective solution to localization and mapping for cluttered environments and is reliable to dynamic changes in the environment Finally, NorthStar uses IR projections onto a surface to estimate the robots pose based on triangulation. We give examples of concept prototypes as well as commercial products such as Sonys Aibo, which have incorporated these technologies in order to improve product utility and value.

SIFT-ing through features with ViPR

Recent advances in computer vision have given rise to a robust and invariant visual pattern recognition technology that is based on extracting a set of characteristic features from an image. Such features are obtained with the scale invariant feature transform (SIFT) which represents the variations in brightness of the image around the point of interest. Recognition performed with these features has been shown to be quite robust in realistic settings. This paper describes the application of this particular visual pattern recognition (ViPR) technology to a variety of robotics applications: object recognition, navigation, manipulation, and human-machine interaction. The paper also describes the technology in more detail and presents a business case for visual pattern recognition in the field of robotics and automation

ERSP: a software platform and architecture for the service robotics industry

In this paper we describe the need for, and the characteristics of a software architecture for commercial robotic products. We describe the Evolution Robotics Software Platform (ERSP/spl trade/), which provides a commercial-grade software architecture for mobile robots. The architecture has been designed to be modular, scalable, lightweight, portable, and reusable. It follows a hybrid model of data flow, combining behavior-based processing modules for real-time reactions with event-based task planning routines. We provide a detailed description of the main architectural components and some basic usage studies. We also highlight areas where compromises have been made and for which continuing work is needed.

Indexing in large scale image collections: Scaling properties and benchmark

Indexing quickly and accurately in a large collection of images has become an important problem with many applications. Given a query image, the goal is to retrieve matching images in the collection. We compare the structure and properties of seven different methods based on the two leading approaches: voting from matching of local descriptors vs. matching histograms of visual words, including some new methods. We derive theoretical estimates of how the memory and computational cost scale with the number of images in the database. We evaluate these properties empirically on four real-world datasets with different statistics. We discuss the pros and cons of the different methods and suggest promising directions for future research.

Structure from stereo vision using unsynchronized cameras for simultaneous localization and mapping

This paper presents a system for automatic reconstruction of 3D structure using two unsynchronized cameras. Three images are acquired sequentially from the left, right, and again from the left camera. A virtual image from the left camera synchronized with the right image is created by interpolating matching points of interest (SIFT features) in the two left images. Both geometric and probabilistic criteria are used to select the correct set of matching features amongst the three views. In an indoor environment, the method typically results in 3D structure with approximately 200 feature points, with a median 3D accuracy of 1.6 cm when the average depth is 3 m and the robot has moved 1-2 cm between each image acquisition.

Monocular graph SLAM with complexity reduction

We present a graph-based SLAM approach, using monocular vision and odometry, designed to operate on computationally constrained platforms. When computation and memory are limited, visual tracking becomes difficult or impossible, and map representation and update costs must remain low. Our system constructs a map of structured views using only weak temporal assumptions, and performs recognition and relative pose estimation over the set of views. Visual observations are fused with differential sensors in an incrementally optimized graph representation. Using variable elimination and constraint pruning, the graph complexity and storage is kept linear in explored space rather than in time. We evaluate performance on sequences with ground truth, and also compare to a standard graph SLAM approach.

Core technologies for service robotics

Service robotics products are becoming a reality. This paper describes three core technologies that enable the next generation of service robots. They are low-cost, make use of low-cost hardware, and prepare for a short time-to-market for product development. The first technology is an object recognition system, which can be used by the robot to interact with the environment The second technology is a vision-based navigation system (vSLAM/spl trade/), which simultaneously can build a map and localize the robot in the map. Finally, the third technology is a flexible and rich software platform (ERSP/spl trade/) that assists developers in rapid design and prototyping of robotics applications.