Robot Perception / Control 2012-08-09

Calibration

Andrea Censi and Davide Scaramuzza. Calibration by correlation using metric embedding from non-metric similarities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35:2357–2370, 10 2013. pdfdoi supp. materialbibtex

Davide and I explain how to calibrate a generic single-view-point camera by only waving it around.

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Andrea Censi, Adam Nilsson, and Richard M. Murray. Motion planning in observations space with learned diffeomorphism models. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2860–2867. Karlsruhe, Germany, 5 2013. pdfdoi supp. materialbibtex

Using learned diffeomorphism models of the dynamics of cameras and range-finders, we formulate motion planning as a planning problem in the observations space. Nodes/states are (uncertain) images; actions/edges are (uncertain) diffeomorphisms.

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Localization/SLAM

Luca Carlone, Andrea Censi, and Frank Dellaert. Coherent measurements selection via l1 relaxation: an approach to robust estimation over graphs. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2014. bibtex

Luca, Frank and I show how outliers in planar pose graph optimization can be detected using convex relaxation. This builds on previous papers showing that planar pose graph optimization can be decoupled in linear problems (with a few tricks).

Luca Carlone and Andrea Censi. From Angular Manifolds to the Integer Lattice: Guaranteed Orientation Estimation with Application to Pose Graph Optimization. IEEE Transactions on Robotics, April 2014. pdfdoi supp. material slidesbibtex

Pose optimization is what is used in SLAM to optimize the map after pose-pose and pose-features correspondences have been established. The variables in this problem are poses living on the nodes of a graph, and measurements are relative measurements along the graph edges. The problem is hard because orientations live on a manifold with nontrivial topology, which makes the problem nonlinear, nonconvex, and with multiple minima. Luca and I try to solve the subproblem of orientation estimation. We find a way to convert the problem to an unconstrained optimization problem on integers. This makes it possible to solve the problem globally and return all likely guesses for the orientation.

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Paloma Puente and Andrea Censi. Dense map inference with user-defined priors: from priorlets to scan eigenvariations. In Cyrill Stachniss, Kerstin Schill, and David Uttal, editors, Spatial Cognition VIII, volume 7463 of Lecture Notes in Computer Science, 94–113. Springer Berlin Heidelberg, August 2012. pdfdoi slidesbibtex

Paloma and I study how to integrate in the mapping problem "rich" information about the environment structure. We use the concept of priorlets to capture a variety of environment priors. We recover the map by optimizing at the same time for continuous and discrete variables (measurements and topology, respectively).

Davide Scaramuzza, Andrea Censi, and Kostas Daniilidis. Exploiting motion priors in visual odometry for vehicle-mounted cameras with non-holonomic constraints. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). San Francisco, CA, September 2011. pdfdoibibtex

Fast and precise visual odometry by exploiting the motion priors specific to the dynamics of a car.

Andrea Censi. On achievable accuracy for pose tracking. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Kobe, Japan, May 2009. pdfdoi supp. material slidesbibtex

In principle, how precise can a scan-matching method be? This paper gives a very simple relation between the achievable accuracy for localization (when the map is known) and the accuracy for scan-matching (when the map is not known).

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Stefano Carpin and Andrea Censi. An experimental assessment of the hsm3d algorithm for sparse and colored data. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3595–3600. St. Louis, MO, October 2009. doibibtex

Adding color to HSM3D.

Andrea Censi and Stefano Carpin. HSM3D: feature-less global 6DOF scan-matching in the Hough/Radon domain. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Kobe, Japan, May 2009. pdfdoi supp. material slidesbibtex

Stefano and I try to do HSM in 3D: much more difficult!

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Andrea Censi. An ICP variant using a point-to-line metric. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi supp. material slidesbibtex

An extremely fast and precise ICP variant for range-finder scan matching, which converges quadratically in a finite number of steps. The implementation is available and included also in ROS.

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Andrea Censi and Gian Diego Tipaldi. Lazy localization using the Frozen-Time Smoother. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi supp. materialbibtex

Global localization without the worries and anxiety of filtering.

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Andrea Censi. An accurate closed-form estimate of ICP's covariance. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 3167–3172. Rome, Italy, April 2007. pdfdoi supp. material slidesbibtex

This paper describes a proper way to compute the covariance of an ICP estimate, which is shown to be superior to the existing algernatives.

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Andrea Censi. On achievable accuracy for range-finder localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 4170–4175. Rome, Italy, April 2007. pdfdoi supp. material slidesbibtex

How precise can a localization method be? This paper derives the Cramer-Rao lower bound for localization with range finders.

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Andrea Censi. Scan matching in a probabilistic framework. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2291–2296. Orlando, Florida, May 2006. pdfdoi supp. material slidesbibtex

Andrea Censi, Luca Iocchi, and Giorgio Grisetti. Scan matching in the Hough domain. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2739–2744. Barcelona, Spain, 2005. pdfdoi supp. material slidesbibtex

A global, complete algorithm for scan matching based on the Hough/Radon transform. This was my Bachelor's thesis.

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