PROBE-GK: Predictive Robust Estimation using Generalized Kernels

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16), Stockholm, Sweden, May 16-21, 201

The design and implementation of a laser range-finder array for robotics applications

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.We introduce the concept of using a laser range finder array to measure height and tilt for mobile robotics applications. We then present a robust, scalable algorithm for extracting height and tilt measurements from the range finder data. We calibrate the sensors using a precision two-axis system, and evaluate the capabilities of the sensors. Finally, we utilize the sensors and the two-axis system for imaging to illustrate their accuracy.by Will Vega-Brown.S.B

Nonparametric Bayesian Inference on Multivariate Exponential Families

We develop a model by choosing the maximum entropy distribution from the set of models satisfying certain smoothness and independence criteria; we show that inference on this model generalizes local kernel estimation to the context of Bayesian inference on stochastic processes. Our model enables Bayesian inference in contexts when standard techniques like Gaussian process inference are too expensive to apply. Exact inference on our model is possible for any likelihood function from the exponential family. Inference is then highly efficient, requiring only O (log N) time and O (N) space at run time. We demonstrate our algorithm on several problems and show quantifiable improvement in both speed and performance relative to models based on the Gaussian process.United States. Office of Naval Research (N00014-09-1-1052)United States. Office of Naval Research (N00014-10-1-0936

Predictive parameter estimation for Bayesian filtering

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-117).In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the raw sensor measurement, and provide a method to learn this function from data. This method is computationally cheap, asymptotically correct, easy to extend to new sensors, and noninvasive, in the sense that it augments, rather than disrupts, existing filtering algorithms. I additionally present two important variants; first, I extend CELLO to learn even when ground truth vehicle states are unavailable; and second, I present an equivalent Bayesian algorithm. I then use CELLO to learn covariance models for several systems, including a laser scan-matcher, an optical flow system, and a visual odometry system. I show that filtering using covariances predicted by CELLO can quantitatively improve estimator accuracy and consistency, both relative to a fixed covariance model and relative to carefully tuned domain-specific covariance models.by William Vega-Brown.S.M

Sensor-Based Reactive Symbolic Planning in Partially Known Environments

This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations. For more information: Kod*la

Sensor-Based Reactive Execution of Symbolic Rearrangement Plans by a Legged Mobile Manipulator

We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user\u27s desired configuration. The robot\u27s behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks. For more information: Kod*la

Markov Chain Hallway and Poisson Forest Environment Generating Distributions

We document two environment-generating distributions used for sampling random 2D maps. The first generates random hallway environments based on a Markov chain and the second generates random forest environments based on the Poisson distribution

CELLO: A fast algorithm for Covariance Estimation

We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate. © 2013 IEEE.United States. National Aeronautics and Space AdministrationSiemens Corporate ResearchUnited States. Office of Naval Research. Multidisciplinary University Research InitiativeMicro Autonomous Consortium Systems and Technolog

Individual differences in switching and inhibition predict perspective-taking across the lifespan

Studies exploring the influence of executive functions (EF) on perspective-taking have focused on inhibition and working memory in young adults or clinical populations. Less consideration has been given to more complex capacities that also involve switching attention between perspectives, or to changes in EF and concomitant effects on perspective-taking across the lifespan. To address this, we assessed whether individual differences in inhibition and attentional switching in healthy adults (ages 17–84) predict performance on a task in which speakers identified targets for a listener with size-contrasting competitors in common or privileged ground. Modification differences across conditions decreased with age. Further, perspective taking interacted with EF measures: youngest adults’ sensitivity to perspective was best captured by their inhibitory performance; oldest adults’ sensitivity was best captured by switching performance. Perspective-taking likely involves multiple aspects of EF, as revealed by considering a wider range of EF tasks and individual capacities across the lifespan