TechTalks from event: Technical session talks from ICRA 2012

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Localization and Mapping

  • Point Set Registration through Minimization of the L2 Distance between 3D-NDT Models Authors: Stoyanov, Todor; Magnusson, Martin; Lilienthal, Achim, J.
    Point set registration --- the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three-Dimensional Normal Distributions Transforms. 3D-NDT models --- a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3D-NDT representations of the input point sets and then formulates an objective function based on the $L_2$ distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.
  • Consistency Analysis for Sliding-Window Visual Odometry Authors: Dong-Si, Tue-Cuong; Mourikis, Anastasios
    In this paper we focus on the problem of {em visual odometry}, i.e., the task of tracking the pose of a moving platform using visual measurements. In recent years, several VO algorithms have been proposed that employ nonlinear minimization in a sliding window of poses for this task. Through the use of iterative re-linearization, these methods are capable of successfully addressing the nonlinearity of the measurement models, and have become the de-facto standard for high-precision VO. In this work, we conduct an analysis of the properties of marginalization, which is the process through which older states are removed from the sliding window. This analysis shows that the standard way of marginalizing older poses results in an erroneous change in the rank of the measurements' information matrix, and leads to underestimation of the uncertainty of the state estimates. Based on the analytical results, we also propose a simple modification of the way in which the measurement Jacobians are computed. This modification avoids the above problem, and results in an algorithm with superior accuracy, as demonstrated in both simulation tests and real-world experiments.
  • Efficient Visual Odometry Using a Structure-Driven Temporal Map Authors: Martinez-Carranza, Jose; Calway, Andrew
    We describe a method for visual odometry using a single camera based on an EKF framework. Previous work has shown that filtering based approaches can achieve accuracy performance comparable to that of optimisation methods providing that large numbers of features are used. However, computational requirements are signicantly increased and frame rates are low. We address this by employing higher level structure - in the form of planes - to efficiently parameterise features and so reduce the filter state size and computational load. Moreover, we extend a 1-point RANSAC outlier rejection method to the case of features lying on planes. Results of experiments with both simulated and real-world data demonstrate that the method is effective, achieving comparable accuracy whilst running at significantly higher frame rates.
  • Using Depth in Visual Simultaneous Localisation and Mapping Authors: Scherer, Sebastian Andreas; Dube, Daniel; Zell, Andreas
    We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the freely available software “Parallel Tracking and Mapping” (PTAM) by Georg Klein, which was originally developed for augmented reality applications. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.
  • A Visual Marker for Precise Pose Estimation Based on Lenticular Lenses Authors: Tanaka, Hideyuki; Sumi, Yasushi; Matsumoto, Yoshio
    Visual marker is a useful assistive tool for service robots. Existing planar visual markers have poor accuracy and stability in pose estimation, especially in frontal direction. In this study, we developed a novel visual marker based on a new principle enabling accurate and stable pose estimation even by observation from frontal direction. The marker has moire patterns which consist of lenticular lens and stripe pattern, which vary their appearance according to visual-line angle of observation. We can extract pose information from the pattern by a single camera. We developed a prototype of the marker and an algorithm for pose estimation, and then demonstrated its superiority to existing markers by some validation tests.
  • Robot Semantic Mapping through Wearable Sensor-Based Human Activity Recognition Authors: Sheng, Weihua; Li, Gang; Zhu, Chun; Du, Jianhao; Cheng, Qi
    Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.