Publications 2012-11-30

For a summary of my main research interests, see a recent presentation about my work.

Legend: slides presented at a conference or other meeting;  datasets or source code.
Please feel free to ask for a PDF if it is not linked here.

Preprints / Working papers

Any feedback on preprints is greatly appreciated! The quantum of feedback you can give is something along the lines of "I stopped reading because I got lost at point X".

Andrea Censi. A mathematical theory of co-design. Technical Report, Laboratory for Information and Decision Systems, MIT, September 2016. Submitted and conditionally accepted to IEEE Transactions on Robotics. pdf supp. materialbibtex

This paper introduces a theory of co-design that describes "design problems", defined as tuples of "functionality space", "implementation space", and "resources space", together with a feasibility relation that relates the three spaces. Design problems can be interconnected together to create "co-design problems", which describe possibly recursive co-design constraints among subsystems.

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Journal papers

Andrea Censi. Uncertainty in monotone co-design problems. IEEE Robotics and Automation Letters, February 2017. pdf supp. materialbibtex

This paper concerns the introduction of uncertainty in the MCDP framework. Uncertainty has two roles: first, it allows to deal with limited knowledge in the models; second, it also can be used to generate consistent relaxations of a problem, as the computation requirements can be lowered should the user accept some uncertainty in the answer.

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Andrea Censi. A class of co-design problems with cyclic constraints and their solution. IEEE Robotics and Automation Letters, 2(1):96–103, Jan 2016. Superseded by preprint ``A Mathematical Theory of Co-Design''. doibibtex

Superseded by preprint ``A Mathematical Theory of Co-Design''.

Andrea Censi and Richard M. Murray. Bootstrapping bilinear models of Simple Vehicles. International Journal of Robotics Research, 34:1087–1113, July 2015. pdfdoi video supp. material slidesbibtex

For a bootstrapping agent connected to an unknown body, the "world" is the series of the unknown actuators, the external environment, and the unknown sensors. One ability that a bootstrapping agent needs is estimating a predictive model of the observations  as a function of the commands. This paper considers the sensorimotor dynamics learning problem for a subset of the "set of all robots" called the Simple Vehicles, which are the idealization of mobile robots equipped with one of the three exteroceptive sensors: field-sampler, vision sensor, and range-finder.

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Sawyer B. Fuller, Michael Karpelson, Andrea Censi, Kevin Y. Ma, and Robert J. Wood. Controlling free flight of a robotic fly using an onboard vision sensor inspired by insect ocelli. Journal of the Royal Society Interface, August 2014. video supp. materialbibtex

This paper describes the first sensory-controlled flight of the Robobee. The Robobee is able to stabilize itself using the feedback from a sensor inspired by the insects' ocelli.

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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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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*, Andrew D. Straw*, Rosalyn W. Sayaman, Richard M. Murray, and Michael H. Dickinson. Discriminating external and internal causes for saccade initiation in freely flying Drosophila. PLOS Computational Biology, February 2013. pdfdoi supp. material slidesbibtex

What goes on in a fruit fly's head while it flies? In this paper we try to identify the dependence of the decision processes on the external stimulus experienced by the fly. Remarkably, we are able to say a great deal about internal sensory processing by just observing the external behavior.

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Andrea Censi, Antonio Franchi, Luca Marchionni, and Giuseppe Oriolo. Simultaneous calibration of odometry and sensor parameters for mobile robots. IEEE Transactions on Robotics, 29(2):475–492, April 2013. pdfdoi supp. materialbibtex

Fast, practical, and extremely precise simultaneous estimation of odometry and sensor configuration parameters for mobile robots.

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Andrea Censi. Kalman filtering with intermittent observations: convergence for semi-markov chains and an intrinsic performance measure. IEEE Transactions on Automatic Control, February 2011. pdfdoibibtex

This paper considers the problem of linear filtering over unreliable channels. Previous research by Sinopoli et al. has shown that there is a threshold on the observations arrival probability that distinguishes between a "stable" and an "unstable" behavior. This paper shows that those conclusions are misleading and only depend on a particular choice of the loss function. In fact, the probability distribution of the filter state exists for each nonzero arrival probability.

Dissertation

Andrea Censi. Bootstrapping Vehicles: a formal approach to unsupervised sensorimotor learning based on invariance. Technical Report, California Institute of Technology, 2012. pdf supp. material slidesbibtex

Could a "brain in a vat" be able to control an unknown robotic body to which it is connected, and use it to achieve useful tasks, without any prior assumptions on the body's sensors and actuators? In this work, the problem of "bootstrapping" is studied in the context of the Vehicles universe, which is an idealization of simple mobile robots, after the work of Braitenberg. The first thread of results consists in analyzing such simple sensorimotor cascades and proposing models of varying complexity that can be learned from data. The second thread regards how to properly formalize the notions of "absence of assumptions", as a particular form of invariance that the bootstrapping agent must satisfy, and proposes some invariance-based design techniques.

This is version 1.3 of my dissertation -- after turning it in, I am still working to integrate the newer material that I had not the time to add. So any comments/suggestions are welcome!

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Conference papers

Liam Paull, Jacopo Tani, Heejin Ahn, Javier Alonso-Mora, Luca Carlone, Michal Cap, Yu Fan Chen, Changhyun Choi, Jeff Dusek, Daniel Hoehener, Shih-Yuan Liu, Michael Novitzky, Igor Franzoni Okuyama, Jason Pazis, Guy Rosman, Valerio Varricchio, Hsueh-Cheng Wang, Dmitry Yershov, Hang Zhao, Michael Benjamin, Christopher Carr, Maria Zuber, Sertac Karaman, Emilio Frazzoli, Domitilla Del Vecchio, Daniela Rus, Jonathan How, John Leonard, and Andrea Censi. Duckietown: an open and inexpensive and flexible platform for autonomy education and research. In IEEE International Conference on Robotics and Automation (ICRA). Singapore, May 2017. pdf supp. materialbibtex

This paper describes the Duckiebot and its software. With 29 authors, we made the record for a robotics conference.

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Jacopo Tani, Liam Paull, Maria Zuber, Daniela Rus, Jonathan How, John Leonard, and Andrea Censi. Duckietown: an innovative way to teach autonomy. In EduRobotics 2016. Athens, Greece, December 2016. pdf supp. materialbibtex

This paper describes the course design for Duckietown: learning objectives, teaching methods, etc.

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Andrea Censi. Monotone co-design problems; or, everything is the same. In Proceedings of the American Control Conference (ACC). 2016. Superseded by preprint ``A Mathematical Theory of Co-Design''. doibibtex

Superseded by preprint ``A Mathematical Theory of Co-Design''.

Prince Singh, Sze Zheng Yong, Jean Gregoire, Andrea Censi, and Emilio Frazzoli. Stabilization of linear continuous-time systems using neuromorphic vision sensors. In IEEE Conference on Decision and Control (CDC), 3030–3036. Dec 2016. doibibtex

E. Mueller, A. Censi, and E. Frazzoli. Low-latency heading feedback control with neuromorphic vision sensors using efficient approximated incremental inference. In IEEE Conference on Decision and Control (CDC), 992–999. Dec 2015. doibibtex

E. Mueller, A. Censi, and E. Frazzoli. Efficient high speed signal estimation with neuromorphic vision sensors. In International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), 1–8. June 2015. doibibtex

Andrea Censi. Efficient Neuromorphic Optomotor Heading Regulation. In American Control Conference (ACC). Chicago, IL, July 2015. doibibtex

This paper describes a method for regulating the heading of a vehicle based on the feedback from a neuromorphic event-based sensor.

Andrea Censi, Erich Mueller, Emilio Frazzoli, and Stefano Soatto. A Power-Performance Approach to Comparing Sensor Families, with application to comparing neuromorphic to traditional vision sensors. In IEEE International Conference on Robotics and Automation (ICRA). May 2015. bibtex

How to compare two sensors, or generally, two sensor families? Any meaningful comparison depends on the task. This paper shows that it also depends on the sensing power available.

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).

Andrea Censi and Davide Scaramuzza. Low-latency event-based visual odometry. In IEEE International Conference on Robotics and Automation (ICRA). May 2014. pdf supp. material slidesbibtex

This paper describes an event-based visual odometry algorighm which uses a DVS sensor together with a normal camera.

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Andrea Censi, Jonas Strubel, Christian Brandli, Tobi Delbruck, and Davide Scaramuzza. Low-latency localization by active led markers tracking using a dynamic vision sensor. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 891–898. Tokyo,Japan, November 2013. pdfdoi supp. material slidesbibtex

Adam Nilsson and Andrea Censi. Accurate recursive learning of uncertain diffeomorphism dynamics. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo, Japan, November 2013. pdfdoi supp. materialbibtex

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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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).

Andrea Censi and Richard M. Murray. Learning diffeomorphism models of robotic sensorimotor cascades. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Saint Paul, MN, May 2012. pdfdoi supp. material slidesbibtex

We show that diffeomorphisms can represent the dynamics of both range-finders as well as cameras, and can be easily learned from raw sensorimotor data. A follow-up paper shows how to do planning in observations space with these learned diffeomorphisms.

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(best student
paper finalist)

Andrea Censi, Magnus Hakansson, and Richard M. Murray. Fault detection and isolation from uninterpreted data in robotic sensorimotor cascades. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Saint Paul, MN, May 2012. pdfdoi supp. material slidesbibtex

This paper shows how bootstrapped low-level sensorimotor models, such as BDS and BGDS can be used for faults and anomaly detection in sensorimotor cascades. The concept of usefulness allows to do detection without having a model for the healthy system.

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Andrea Censi and Richard M. Murray. Uncertain semantics, representation nuisances, and necessary invariance properties of bootstrapping agents. In Joint IEEE International Conference on Development and Learning and Epigenetic Robotics. Frankfurt, Germany, August 2011. pdfdoi slidesbibtex

This paper tries to describe formally the idea of "uncertain semantics" in a bootstrapping problem by using the mathematics of group actions. If interested, please see Part 1 and Part 3 of my dissertation, where these ideas are further expanded and made much more formal than in this short paper.

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 and Richard M. Murray. Bootstrapping sensorimotor cascades: a group-theoretic perspective. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). San Francisco, CA, September 2011. pdfdoi supp. materialbibtex

This paper discusses a class of models (BGDS) that capture the bilinear dynamics of heterogeneous sensors, such as field samplers, cameras, and range-finders, as well as their dependence on the gradient of the observations.

This material has been greatly expanded in my dissertation (Part 2).

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(best conference
paper finalist)

Andrea Censi and Richard M. Murray. Bootstrapping bilinear models of robotic sensorimotor cascades. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Shanghai, China, May 2011. supp. material slidesbibtex

This paper shows that simple bilinear models capture the dynamics of heterogeneous sensors, such as field samplers, cameras, and range-finders. These models can be learned unsupervisedly and used to perform simple tasks.

This material has been greatly expanded in the following paper.

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Shuo Han, Andrea Censi, Andrew D. Straw, and Richard M. Murray. A bio-plausible design for visual pose stabilization. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5679–5686. Taipei, Taiwan, October 2010. pdfdoi video slidesbibtex

Stabilizing hovering for an helicopter using a bio-plausible strategy.

Andrea Censi, Shuo Han, Sawyer B. Fuller, and Richard M. Murray. A bio-plausible design for visual attitude stabilization. In Proceedings of the 48th IEEE Conference on Decision and Control. Shanghai, China, December 2009. pdfdoi slidesbibtex

This paper describes a "bio-plausible" control strategy for stabilizing the attitude in SO(3) of a flying body. The follow-up paper considers the SE(3) case.

Andrea Censi. On the performance of Kalman filtering with intermittent observations: a geometric approach with fractals. In Proceedings of the American Control Conference (ACC). St. Louis, Missouri, June 2009. pdfdoi supp. material slidesbibtex

The ultimate match: Cantor meets Kalman!

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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 and Richard M. Murray. Real-valued consensus over noisy quantized channels. In Proceedings of the American Control Conference (ACC). St. Louis, Missouri, June 2009. pdfdoi supp. material slidesbibtex

How to reach a consensus if you and your friends are a bunch of spiking neurons.

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Daniele Calisi, Andrea Censi, Luca Iocchi, and Daniele Nardi. OpenRDK: a modular framework for robotic software development. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Nice, France, September 2008. pdfdoi supp. material slidesbibtex

Andrea Censi, Daniele Calisi, Alessandro De Luca, and Giuseppe Oriolo. A Bayesian framework for optimal motion planning with uncertainty. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi supp. material slidesbibtex

How to get to your goal without getting lost. This was my Master's degree final thesis.

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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, Luca Marchionni, and Giuseppe Oriolo. Simultaneous maximum-likelihood calibration of robot and sensor parameters. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi video supp. material slidesbibtex

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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Unrefereed papers

S. Bahadori, D. Calisi, A. Censi, A. Farinelli, G. Grisetti, L. Iocchi, and D. Nardi. Intelligent systems for search and rescue. In Proc. of IROS Workshop ``Urban Search and Rescue: from RoboCup to real world applications'' (IROS), Sendai, Japan. 2004. pdfbibtex

In my first lab, we used to do rescue robotics. Those were the days when I was in charge of charging the robot batteries...