Author Archives: Juan-antonio Fernández-madrigal

A new approach to SLAM based on KF but without linearization

December 23, 2017 09:42 ,

Feng Tan, Winfried Lohmiller, and Jean-Jacques Slotine, Analytical SLAM without linearization, The International Journal of Robotics Research
Vol 36, Issue 13-14, pp. 1554 – 1578, DOI: 10.1177/0278364917710541.

This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion that avoids linearized approximations altogether. Based on the creation of virtual synthetic measurements, the algorithm uses a linear time-varying Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, or time-to-contact. SLAM-DUNK, a more advanced version of the algorithm in global coordinates, exploits the conditional independence property of the SLAM problem, decoupling the covariance matrices between different landmarks and reducing computational complexity to O(n). As illustrated in simulations, the proposed algorithm can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.

Posted in: Bayesian filtering, Mobile robot SLAM

A new method for estimating inertial sensor signals

December 14, 2017 09:26 ,

M. Ghobadi, P. Singla and E. T. Esfahani, Robust Attitude Estimation from Uncertain Observations of Inertial Sensors Using Covariance Inflated Multiplicative Extended Kalman Filter, IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 1, pp. 209-217, DOI: 10.1109/TIM.2017.2761230.

This paper presents an attitude estimation method from uncertain observations of inertial sensors, which is highly robust against different uncertainties. The proposed method of covariance inflated multiplicative extended Kalman filter (CI-MEKF) takes the advantage of non-singularity of covariance in MEKF as well as a novel covariance inflation (CI) approach to fuse inconsistent information. The proposed CI approach compensates the undesired effect of magnetic distortion and body acceleration (as inherent biases of magnetometer and accelerometer sensors data, respectively) on the estimated attitude. Moreover, the CI-MEKF can accurately estimate the gyro bias. A number of simulation scenarios are designed to compare the performance of the proposed method with the state of the art in attitude estimation. The results show the proposed method outperforms the state of the art in terms of estimation accuracy and robustness. Moreover, the proposed CI-MEKF method is shown to be significantly robust against different uncertainties, such as large body acceleration, magnetic distortion, and errors, in the initial condition of the attitude.

Posted in: Bayesian filtering , Tagged:

Deep reinforcement learning applied to learn both attention and classification in a task of vehicle classification

December 14, 2017 09:21 ,

D. Zhao, Y. Chen and L. Lv, Deep Reinforcement Learning With Visual Attention for Vehicle Classification, IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 356-367, DOI: 10.1109/TCDS.2016.2614675.

Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks.

Posted in: Computer vision , Tagged: , , ,

Using deep learning for extracting features from range data

December 14, 2017 09:18 ,

Y. Liao, S. Kodagoda, Y. Wang, L. Shi and Y. Liu, Place Classification With a Graph Regularized Deep Neural Network, IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 304-315, DOI: 10.1109/TCDS.2016.2586183.

Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multilayer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

Posted in: Mobile robot mapping , Tagged: ,

Improving the search of matching image features using the usual coherence present in true matches

December 7, 2017 09:12 ,

W. Y. Lin et al, CODE: Coherence Based Decision Boundaries for Feature Correspondence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 1, pp. 34-47, DOI: 10.1109/TPAMI.2017.2652468.

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.

Posted in: Computer vision , Tagged:

A new method for nonlinear optimization aimed to embedded computers, and a nice state of the art of that problem

December 7, 2017 09:08 ,

N. Y. Chiang, R. Huang and V. M. Zavala, An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization, IEEE Transactions on Automatic Control, vol. 62, no. 12, pp. 6110-6121, DOI: 10.1109/TAC.2017.2694806.

We present a filter line-search algorithm for nonconvex continuous optimization that combines an augmented Lagrangian function and a constraint violation metric to accept and reject steps. The approach is motivated by real-time optimization applications that need to be executed on embedded computing platforms with limited memory and processor speeds. The proposed method enables primal-dual regularization of the linear algebra system that in turn permits the use of solution strategies with lower computing overheads. We prove that the proposed algorithm is globally convergent and we demonstrate the developments using a nonconvex real-time optimization application for a building heating, ventilation, and air conditioning system. Our numerical tests are performed on a standard processor and on an embedded platform. We demonstrate that the approach reduces solution times by a factor of over 1000.

Posted in: Mathematics, Real-Time Systems , Tagged: ,

A new spatial transformation for planning mobile robot trajectories that guarantees a solution and also time requirement satisfaction

December 6, 2017 09:04 ,

S. G. Loizou, The Navigation Transformation, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1516-1523, DOI: 10.1109/TRO.2017.2725323.

This work introduces a novel approach to the solution of the navigation problem by mapping an obstacle-cluttered environment to a trivial domain called the point world, where the navigation task is reduced to connecting the images of the initial and destination configurations by a straight line. Due to this effect, the underlying transformation is termed the “navigation transformation.” The properties of the navigation transformation are studied in this work as well as its capability to provide-through the proposed feedback controller designs-solutions to the motion- and path-planning problems. Notably, the proposed approach enables the construction of temporal stabilization controllers as detailed herein, which provide a time abstraction to the navigation problem. The proposed solutions are correct by construction and, given a diffeomorphism from the workspace to a sphere world, tuning free. A candidate construction for the navigation transformation on sphere worlds is proposed. The provided theoretical results are backed by analytical proofs. The efficiency, robustness, and applicability of the proposed solutions are supported by a series of experimental case studies.

Posted in: Robot motion planning , Tagged: ,

A survey in interactive perception in robots: interacting with the environment to improve perception and using internal models and prediction too

December 6, 2017 08:54 ,

J. Bohg et al, Interactive Perception: Leveraging Action in Perception and Perception in Action, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1273-1291, DOI: 10.1109/TRO.2017.2721939.

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Posted in: Psycho-physiological bases of engineering, Robotics , Tagged: ,

Experimental comparison of methods for merging line segments in line-segment-based maps for mobile robots

December 1, 2017 08:50 ,

Francesco Amigoni, Alberto Quattrini Li, Comparing methods for merging redundant line segments in maps, Robotics and Autonomous Systems, Volume 99, 2018, Pages 135-147, DOI: 10.1016/j.robot.2017.10.016.

Map building of indoor environments is considered a basic building block for autonomous mobile robots, enabling, among others, self-localization and efficient path planning. While the mainstream approach stores maps as occupancy grids of regular cells, some works have advocated for the use of maps composed of line segments to represent the boundary of obstacles, leveraging on their more compact size. In order to limit both the growth of the corresponding data structures and the effort in processing these maps, a number of methods have been proposed for merging together redundant line segments that represent the same portion of the environment. In this paper, we experimentally compare some of the most significant methods for merging line segments in maps by applying them to publicly available data sets. At the end, we propose some guidelines to choose the appropriate method.

Posted in: Mobile robot mapping , Tagged: ,

On how psychologists realize that the brain, after all, may be creating symbols (concepts), like deep neural networks show

November 15, 2017 09:24 ,

Jeffrey S. Bowers, Parallel Distributed Processing Theory in the Age of Deep Networks, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 950-961, DOI: 10.1016/j.tics.2017.09.013.

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory.

Posted in: Psycho-physiological bases of engineering , Tagged: ,