Consumer robotic devices provide a platform for embedded computer vision algorithms in applications for everyday life. The consumer market is very price-sensitive, so robots must be developed with a single task in mind, aiming to provide the best performance at the lowest cost. Computational resources in consumer robotics are scarce given cost constraints, forcing the design of novel algorithms that elegantly incorporate such constraints. We present a graph-based SLAM approach designed to operate on computationally constrained platforms using monocular vision and odometry. When computation and memory are limited, visual tracking becomes difficult or impossible, and costs for map representation and updating 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. We fuse visual observations and differential measurements in an incrementally optimized graph representation. Using variable elimination and constraint pruning, graph complexity and storage is kept linear in explored space rather than growing over time. We evaluate performance on sequences with ground truth and also compare to a standard graph-SLAM approach.

Localization in unknown environments using low-cost sensors on embedded hardware is challenging. Yet, it is a requirement for consumer robots if systematic navigation is desired. In this paper, we present a localization approach that learns the spatial variation of an observed continuous signal over the environment. We model the signal as a piecewise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach. By applying the concepts of the exactly sparse extended information filter (ESEIF) , a constant-time, linear-space algorithm is obtained under certain approximations. We apply our framework to a sensor measuring bearing to active beacons, where measurements are distorted because of occlusion and signal reflections. Experimental results from running GraphSLAM, extended Kalman filter SLAM, and ESEIF-SLAM on manually collected sensor measurements, as well as on data recorded on a vacuum-cleaner robot, validate our model. The ESEIF-SLAM solution is evaluated on an ARM 7 embedded board with 64-kB RAM connected to a Roomba 510 vacuum cleaner. The presented methods are also used in Evolution Robotics ' Mint Cleaner product for autonomous floor cleaning.

The constraints of a low-cost consumer product pose a major challenge for designing a localization system. In previous work, we introduced Vector Field SLAM [5], a system for simultaneously estimating robot pose and a vector field induced by stationary signal sources present in the environment. In this paper we show how this method can be realized on a low-cost embedded processing unit by applying the concepts of the Exactly Sparse Extended Information Filter [15]. By restricting the set of active features to the 4 nodes of the current cell, the size of the map becomes linear in the area explored by the robot while the time for updating the state can be held constant under certain approximations. We report results from running our method on an ARM 7 embedded board with 64 kByte RAM controlling a Roomba 510 vacuum cleaner in a standard test environment.

Localization in unknown environments using low-cost sensors remains a challenge. This paper presents a new localization approach that learns the spatial variation of an observed continuous signal. We model the signal as a piece-wise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach. We apply our framework to a sensor measuring bearing to active beacons where measurements are systematically distorted due to occlusion and signal reflections of walls and other objects present in the environment. Experimental results from running GraphSLAM and EKF-SLAM on manually collected sensor measurements as well as on data recorded on a vacuum-cleaner robot validate our model.

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.

While many visual simultaneous localization and mapping (SLAM) systems use point features as landmarks, few take advantage of the edge information in images. Those SLAM systems that do observe edge features do not consider edges with all degrees of freedom. Edges are difficult to use in vision SLAM because of selection, observation, initialization and data association challenges. A map that includes edge features, however, contains higher-order geometric information useful both during and after SLAM. We define a well-localized edge landmark and present an efficient algorithm for selecting such landmarks. Further, we describe how to initialize new landmarks, observe mapped landmarks in subsequent images, and address the data association challenges of edges. Our methods, implemented in a particle-filter SLAM system, operate at frame rate on live video sequences. (C) 2008 Elsevier B.V. All rights reserved.

We present a unified method for recovering from tracking fail ure and closing loops in real time monocular simultaneous localisation and mapping. Within a graph-based map representation, we show that recovery and loop closing both reduce to the creation of a graph edge. We describe and implement a bag-of-words appearance model for ranking potential loop closures, and a robust method for using both structure and image appearance to confirm likely matches. The resulting system closes loops and recovers from failures while mapping thousands of landmarks, all in real time.

We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a heuristic measure of such nonlinearity, using it to guide our traversal of the graph. The system operates in real-time on sequences with several hundreds of landmarks while performing global graph optimization, yielding accurate and nearly consistent estimation relative to offline bundle adjustment, and considerably better consistency than EKF SLAM and FastSLAM.

Unknown environments pose a particular challenge for augmented reality applications because the 3D models required for tracking, rendering and interaction are not available ahead of time. Consequently, authoring of AR content must take place on-line. This work describes a set of techniques to simplify the online authoring of annotations in unknown environments using a simultaneous localisation and mapping (SLAM) system. The point-based SLAM system is extended to specifically track and estimate high-level features indicated by the user. The automatic estimation of these complex landmarks by the system relieves the user from the burden of manually specifying the full 3D pose of annotations while improving accuracy. These properties are especially interesting for remote collaboration applications where either user interfaces on handhelds or camera control by the remote expert are limited.

Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter and top-down search to allow realtime performance while mapping large numbers of landmarks. To our knowledge, we are the first to apply this FastSLAM-type particle filter to single-camera SLAM. We also introduce a novel partial initialization procedure that efficiently determines the depth of new landmarks. Moreover, we use information available in observations of new landmarks to improve camera pose estimates. Results show the system operating in real-time on a standard workstation while mapping hundreds of landmarks.

Paper-based cartographic maps provide highly detailed information visualisation with unrivalled fidelity and information density. Moreover, the physical properties of paper afford simple interactions for browsing a map or focusing on individual details, managing concurrent access for multiple users and general malleability. However, printed maps are static displays and while computer-based map displays can support dynamic information, they lack the nice properties of real maps identified above. We address these shortcomings by presenting a system to augment printed maps with digital graphical information and user interface components. These augmentations complement the properties of the printed information in that they are dynamic, permit layer selection and provide complex computer mediated interactions with geographically embedded information and user interface controls. Two methods are presented which exploit the benefits of using tangible artifacts for such interactions.

Scalability is the primary challenge to studying large complex network systems with network emulation. This paper studies topology partitioning, assigning disjoint pieces of the network topology across processors, as a technique to increase emulation capacity with increasing hardware resources. We develop methods to create partitions based on expected communication across the topology. Our evaluation methodology quantifies the communication overhead or efficiency of the resulting partitions. We implement and contrast three partitioning strategies in ModelNet, a large-scale network emulator, using different topologies and uniform communication patterns. Results show that standard graph partitioning algorithms can double the efficiency of the emulation for Internet-like topologies relative to random partitioning.

This paper describes initial results regarding navigation and control algorithms developed for Gamera, an autonomous underwater vehicle. In order to perform high precision controlled motions, the robot employs a novel propulsion system known as Nektons, and carries a comprehensive sensor suite to track orientation and position. A model of the robot has been developed that relates the 12 robot controlled propulsion outputs-frequency, amplitude, and center point of oscillation for each of the four motors-to the forces generated by the entire system. We explore the rationalization of control algorithms for this system. Using these algorithms, Gamera demonstrates a high level of control and successfully performs pre-specified maneuvers autonomously.