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  • Andrea Censi
  • 2008
This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual(More)
  • Andrea Censi
  • 2007
Existing methods for estimating the covariance of the ICP (iterative closest/corresponding point) algorithm are either inaccurate or are computationally too expensive to be used online. This paper proposes a new method, based on the analysis of the error function being minimized. It considers that the correspondences are not independent (the same(More)
Intense efforts to define a common structure in robotic applications, both from a conceptual and from an implementation point of view, have been carried out in the last years and several frameworks have been realized for helping in developing robotic applications. However, due to the diversity of these applications, as well as of the research groups(More)
Scaling a flying robot down to the size of a fly or bee requires advances in manufacturing, sensing and control, and will provide insights into mechanisms used by their biological counterparts. Controlled flight at this scale has previously required external cameras to provide the feedback to regulate the continuous corrective manoeuvres necessary to keep(More)
—This technical note shows that the stationary distribution for the covariance of Kalman filtering with intermittent observations exists under mild conditions for a very general class of packet dropping models (semi-Markov chain). These results are proved using the geometric properties of Riccati recursions with respect to a particular Riemannian distance.(More)
This paper presents HSM3D, an algorithm for global rigid 6DOF alignment of 3D point clouds. The algorithm works by projecting the two input sets into the Radon/Hough domain, whose properties allow to decompose the 6DOF search into a series of fast one-dimensional cross-correlations. No planes or other particular features must be present in the input data,(More)
  • Andrea Censi
  • 2007
The covariance of every unbiased estimator is bounded by the Cramer-Rao lower bound, which is the inverse of Fisher's information matrix. This paper shows that, for the case of localization with range-finders, Fisher's matrix is a function of the expected readings and of the orientation of the environment's surfaces at the sensed points. The matrix also(More)
Scan matching is used as a building block in many robotic applications, for localization and simultaneous localization and mapping (SLAM). Although many techniques have been proposed for scan matching in the past years, more efficient and effective scan matching procedures allow for improvements of such associated problems. In this paper we present a new(More)
For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for(More)