3 research outputs found

    A single landmark based localization algorithm for non-holonomic mobile robots

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    International audienceThis paper proposes a single landmark based localization algorithm for non-holonomic mobile robots. In the case of a unicycle robot model, the localization problem is equivalent to the system observability. Based on this observation, the proposed localization method consists in finding a vector function which depends on the measurement vector and its derivatives. In order to compute estimates of the successive derivatives of the measurement vector, we will use a numerical differentiation method. When the robot is able to only measure the relative angle between itself and the landmark in 2D case, the algorithm estimates the posture of the robot, under the hypothesis that control inputs are known. But, sometimes it is also useful to be able to estimate the control input (for example when the robot slips). This is possible with the proposed algorithm by using a landmark in dimension three. The simulation results will be given in order to show the effectiveness of the proposed algorithm. Moreover, these results are compared with those obtained by an Extended Kalman Filter in order to underline the advantages of the new algorithm

    Metric localization using a single artificial landmark for indoor mobile robots

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    Abstract – We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time. Index Terms – metric localization, artificial landmark, catadioptric vision, EKF I

    Information-theoretic environment modeling for mobile robot localization

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    To enhance robotic computational efficiency without degenerating accuracy, it is imperative to fit the right and exact amount of information in its simplest form to the investigated task. This thesis conforms to this reasoning in environment model building and robot localization. It puts forth an approach towards building maps and localizing a mobile robot efficiently with respect to unknown, unstructured and moderately dynamic environments. For this, the environment is modeled on an information-theoretic basis, more specifically in terms of its transmission property. Subsequently, the presented environment model, which does not specifically adhere to classical geometric modeling, succeeds in solving the environment disambiguation effectively. The proposed solution lays out a two-level hierarchical structure for localization. The structure makes use of extracted features, which are stored in two different resolutions in a single hybrid feature-map. This enables dual coarse-topological and fine-geometric localization modalities. The first level in the hierarchy describes the environment topologically, where a defined set of places is described by a probabilistic feature representation. A conditional entropy-based criterion is proposed to quantify the transinformation between the feature and the place domains. This criterion provides a double benefit of pruning the large dimensional feature space, and at the same time selecting the best discriminative features that overcome environment aliasing problems. Features with the highest transinformation are filtered and compressed to form a coarse resolution feature-map (codebook). Localization at this level is conducted through place matching. In the second level of the hierarchy, the map is viewed in high-resolution, as consisting of non-compressed entropy-processed features. These features are additionally tagged with their position information. Given the identified topological place provided by the first level, fine localization corresponding to the second level is executed using feature triangulation. To enhance the triangulation accuracy, redundant features are used and two metric evaluating criteria are employ-ed; one for dynamic features and mismatches detection, and another for feature selection. The proposed approach and methods have been tested in realistic indoor environments using a vision sensor and the Scale Invariant Feature Transform local feature extraction. Through experiments, it is demonstrated that an information-theoretic modeling approach is highly efficient in attaining combined accuracy and computational efficiency performances for localization. It has also been proven that the approach is capable of modeling environments with a high degree of unstructuredness, perceptual aliasing, and dynamic variations (illumination conditions; scene dynamics). The merit of employing this modeling type is that environment features are evaluated quantitatively, while at the same time qualitative conclusions are generated about feature selection and performance in a robot localization task. In this way, the accuracy of localization can be adapted in accordance with the available resources. The experimental results also show that the hybrid topological-metric map provides sufficient information to localize a mobile robot on two scales, independent of the robot motion model. The codebook exhibits fast and accurate topological localization at significant compression ratios. The hierarchical localization framework demonstrates robustness and optimized space and time complexities. This, in turn, provides scalability to large environments application and real-time employment adequacies
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