Robust observer design under measurement noise with gain adaptation and saturated estimates

We use incremental homogeneity, gain adaptation and incremental observability for proving new results on robust observer design for systems with noisy measurement and bounded trajectories. A state observer is designed by dominating the incrementally homogeneous nonlinearities of the observation error system with its linear approximation, while gain adaptation and incremental observability guarantee an asymptotic upper bound for the estimation error depending on the limsup of the norm of the measurement noise. A characteristic and innovative feature of this observer is the mixed low/high-gain structure in combination with saturated state estimates and dynamically tuned gains and saturation levels. The gain adaptation is implemented as the output of a stable filter using the squared norm of the measured output estimation error and the mismatch between each estimate and its saturated value

Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.Comment: Submitted to IROS 2012. 8 page

Sequential processing and performance optimization in nonlinear state estimation

We propose a framework for designing observers for noisy nonlinear systems with global convergence properties and performing robustness and noise sensitivity. Our state observer is the result of the combination of a state norm estimator with a bank of Kalman-type lters, parametrized by the state norm estimator. The state estimate is sequentially processed through the bank of lters. In general, existing nonlinear state observers are responsible for estimation errors which are sensitive to model uncertainties and measurement noise, depending on the initial state conditions. Each Kalman-type lter of the bank contributes to improve the estimation error performances to a certain degree in terms of sensitivity with respect to noise and initial state conditions. A sequential processing algorithm for performance optimization is given and simulations show the eectiveness of these sequential lters

Continuously-implemented sliding-mode adaptive unknown-input observers under noisy measurements

International audienceWe propose an estimator for nonlinear systems with unmatched unknown inputs and under measurement noise. The estimator design is based on the combination of observer design for descriptor systems, sliding-modes theory and adaptive control. The estimation of the measurement noise is achieved thanks to the transformation of the original system into a singular form where the measurement noise makes part of the augmented state. Two adaptive parameters are updated online, one to compensate for the unknown bounds on the states, the unknown inputs and the measurement noise and a second one to compensate for the effect of the nonlinearities. To join robust state estimation and unknown-inputs reconstruction, our approach borrows inspiration from sliding-mode theory however, all signals are continuously implemented. We demonstrate that both state and unknown-inputs estimation are achieved up to arbitrarily small tolerance. The utility of our theoretical results is illustrated through simulation case-studies

Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome

Global finite-time observers for non linear systems

International audienceA global finite-time observer is designed for nonlinear systems which are uniformly observable and globally Lipschitz. This result is based on a high-gain approach