Faster long-term SLAM through direct use of Lie groups in filtering

May 21, 2018 07:52 ,

Kruno Lenac, Josip Ćesić, Ivan Marković, and Ivan Petrović, Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM, The International Journal of Robotics Research Vol 37, Issue 6, pp. 585 – 610 DOI: 10.1177/0278364918767756.

In this paper we propose a simultaneous localization and mapping (SLAM) back-end solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive LG-ESDSF and demonstrate that it retains all the good characteristics of the classic Euclidean ESDSF, the main advantage being the exact sparsity of the information matrix. The key advantage of LG-ESDSF in comparison with the classic ESDSF lies in the ability to respect the state space geometry by negotiating uncertainties and employing filtering equations directly on Lie groups. We also exploit the special structure of the information matrix in order to allow long-term operation while the robot is moving repeatedly through the same environment. To prove the effectiveness of the proposed SLAM solution, we conducted extensive experiments on two different publicly available datasets, namely the KITTI and EuRoC datasets, using two front-ends: one based on the stereo camera and the other on the 3D LIDAR. We compare LG-ESDSF with the general graph optimization framework (g2o) when coupled with the same front-ends. Similarly to g2o the proposed LG-ESDSF is front-end agnostic and the comparison demonstrates that our solution can match the accuracy of g2o, while maintaining faster computation times. Furthermore, the proposed back-end coupled with the stereo camera front-end forms a complete visual SLAM solution dubbed LG-SLAM. Finally, we evaluated LG-SLAM using the online KITTI protocol and at the time of writing it achieved the second best result among the stereo odometry solutions and the best result among the tested SLAM algorithms.

Posted in: Mobile robot SLAM , Tagged: , ,

On the effects of delays in the stability of a network controlled plant due to both clocks not being synchronized

May 17, 2018 07:45 ,

K. Okano, M. Wakaiki, G. Yang and J. P. Hespanha, Stabilization of Networked Control Systems Under Clock Offsets and Quantization, IEEE Transactions on Automatic Control, vol. 63, no. 6, pp. 1708-1723 DOI: 10.1109/TAC.2017.2753938.

This paper studies the impact of clock mismatches and quantization on networked control systems. We consider a scenario where the plant’s state is measured by a sensor that communicates with the controller through a network. Variable communication delays and clock jitter do not permit a perfect synchronization between the clocks of the sensor and controller. We investigate limitations on the clock offset tolerable for stabilization of the feedback system. For a process with a scalar-valued state, we show that there exists a tight bound on the offset above which the closed-loop system cannot be stabilized with any causal controllers. For higher dimensional plants, if the plant has two distinct poles, then the effect of clock mismatches can be canceled with a finite number of measurements, and hence there is no fundamental limitation. We also consider the case where the measurements are subject to quantization in addition to clock mismatches. For first-order plants, we present necessary conditions and sufficient conditions for stabilizability, which show that a larger clock offset requires a finer quantization.

Posted in: Communication networks, Control Engineering , Tagged: , ,

All the information about our cognitive processes that can be deduced from our mouse movements

May 16, 2018 08:35 ,

Paul E. Stillman, Xi Shen, Melissa J. Ferguson, How Mouse-tracking Can Advance Social Cognitive Theory, Trends in Cognitive Sciences, Volume 22, Issue 6, 2018, Pages 531-543 DOI: 10.1016/j.tics.2018.03.012.

Mouse-tracking – measuring computer-mouse movements made by participants while they choose between response options – is an emerging tool that offers an accessible, data-rich, and real-time window into how people categorize and make decisions. In the present article we review recent research in social cognition that uses mouse-tracking to test models and advance theory. In particular, mouse-tracking allows examination of nuanced predictions about both the nature of conflict (e.g., its antecedents and consequences) as well as how this conflict is resolved (e.g., how decisions evolve). We demonstrate how mouse-tracking can further our theoretical understanding by highlighting research in two domains − social categorization and self-control. We conclude with future directions and a discussion of the limitations of mouse-tracking as a method.

Posted in: Cognitive sciences , Tagged:

On how sleep improves our problem-solving capabilities

May 16, 2018 08:33 ,

Penelope A. Lewis, Günther Knoblich, Gina Poe, How Memory Replay in Sleep Boosts Creative Problem-Solving, Trends in Cognitive Sciences, Volume 22, Issue 6, 2018, Pages 491-503 DOI: 10.1016/j.tics.2018.03.009.

Creative thought relies on the reorganisation of existing knowledge. Sleep is known to be important for creative thinking, but there is a debate about which sleep stage is most relevant, and why. We address this issue by proposing that rapid eye movement sleep, or ‘REM’, and non-REM sleep facilitate creativity in different ways. Memory replay mechanisms in non-REM can abstract rules from corpuses of learned information, while replay in REM may promote novel associations. We propose that the iterative interleaving of REM and non-REM across a night boosts the formation of complex knowledge frameworks, and allows these frameworks to be restructured, thus facilitating creative thought. We outline a hypothetical computational model which will allow explicit testing of these hypotheses.

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

Shared autonomy in robot teleoperation where the robot learns the user’s skills to adapt to them

May 11, 2018 08:28 ,

Enayati, N., Ferrigno, G. & De Momi, E. Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method, Auton Robot (2018) 42: 997, DOI: 10.1007/s10514-017-9675-4.

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operator’s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of user’s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking.

Posted in: Human teleoperation , Tagged:

Socially acceptable collision avoidance

May 10, 2018 08:25 ,

Haoan Wang, Antonio Tota, Bilin Aksun-Guvenc, Levent Guvenc Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method, Mechatronics, Volume 50, 2018, Pages 341-355, DOI: 10.1016/j.mechatronics.2017.11.009.

This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper.

Posted in: Robot motion planning , Tagged: , ,

On the use of flipped classroom for control engineering classes and its problem with the required (longer) time for learning

May 10, 2018 08:21 ,

Y. Kim and C. Ahn, Effect of Combined Use of Flipped Learning and Inquiry-Based Learning on a System Modeling and Control Course, IEEE Transactions on Education, vol. 61, no. 2, pp. 136-142, DOI: 10.1109/TE.2017.2774194.

Contribution: This paper illustrates how to design and implement curricula in terms of the combined use of flipped learning and inquiry-based learning in an engineering course. Background: Elementary courses in engineering schools are conventional and foundational, and involve a considerable amount of knowledge. Throughout such courses, students are also expected to develop insight, which cannot be obtained by only listening to instructors. Having relevant discussions is also difficult for most instructors. Intended Outcomes: The combined use of flipped learning and inquiry-based learning would be beneficial to broaden student achievement. Application Design: Based on an epistemological approach about knowledge and knowing, this paper applies the combined use of flipped learning and inquiry-based learning to enhance student knowledge and advance ways of thinking on a System Modeling and Control course. Findings: The extended learning time and the collective responsibility for learning are discussed as critical issues in applying the combined use of flipped learning and inquiry-based learning in an engineering school.

Posted in: Control Engineering, Education , Tagged: ,

Omnidirectional localization

May 9, 2018 08:10 ,

Milad Ramezani, Kourosh Khoshelham, Clive Fraser, Pose estimation by Omnidirectional Visual-Inertial Odometry,Robotics and Autonomous Systems,
Volume 105, 2018, Pages 26-37, DOI: 10.1016/j.robot.2018.03.007.

In this paper, a novel approach to ego-motion estimation is proposed based on visual and inertial sensors, named Omnidirectional Visual-Inertial Odometry (OVIO). The proposed approach combines omnidirectional visual features with inertial measurements within the Multi-State Constraint Kalman Filter (MSCKF). In contrast with other visual inertial odometry methods that use visual features captured by perspective cameras, the proposed approach utilizes spherical images obtained by an omnidirectional camera to obtain more accurate estimates of the position and orientation of the camera. Because the standard perspective model is unsuitable for omnidirectional cameras, a measurement model on a plane tangent to the unit sphere rather than on the image plane is defined. The key hypothesis of OVIO is that a wider field of view allows the incorporation of more visual features from the surrounding environment, thereby improving the accuracy and robustness of the ego-motion estimation. Moreover, by using an omnidirectional camera, a situation where there is not enough texture is less likely to arise. Experimental evaluation of OVIO using synthetic and real video sequences captured by a fish-eye camera in both indoor and outdoor environments shows the superior performance of the proposed OVIO as compared to the MSCKF using a perspective camera in both positioning and attitude estimation.

Posted in: Mobile Robot Localization , Tagged:

Dynamic and efficient occupancy mapping

May 7, 2018 08:08 ,

Vitor Guizilini and Fabio Ramos, Towards real-time 3D continuous occupancy mapping using Hilbert maps, The International Journal of Robotics Research
Vol 37, Issue 6, pp. 566 – 584, DOI: 10.1177/0278364918771476.

The ability to model the surrounding space and determine which areas are occupied is of key importance in many robotic applications, ranging from grasping and manipulation to path planning and obstacle avoidance. Occupancy modeling is often hindered by several factors, such as: real-time constraints, that require quick updates and access to estimates; quality of available data, that may contain gaps and partial occlusions; and memory requirements, especially for large-scale environments. In this work we propose a novel framework that elegantly addresses all these issues, by producing an efficient non-stationary continuous occupancy function that can be efficiently queried at arbitrary resolutions. Furthermore, we introduce techniques that allow the learning of individual features for different areas of the input space, that are better able to model its contained information and promote a higher-level understanding of the observed scene. Experimental tests were conducted on both simulated and real large-scale datasets, showing how the proposed framework rivals current state-of-the-art techniques in terms of computational speed while achieving a substantial decrease (of orders of magnitude) in memory requirements and demonstrating better interpolative powers, that are able to smooth out sparse and noisy information.

Posted in: Mobile robot mapping , Tagged:

A nice analysis of the particularities of deep learning when applied to robotics, where the need to act is principal (unlike in other disciplines such as computer vision)

April 28, 2018 07:51 ,

Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, and Peter Corke, The limits and potentials of deep learning for robotics, The International Journal of Robotics Research Vol 37, Issue 4-5, pp. 405 – 420, DOI: 10.1177/0278364918770733.

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

Some quotes beyond the abstract:

Deep learning systems, e.g. for classification or detection, typically return scores from their softmax layers that are proportional to the system’s confidence, but are not calibrated probabilities, and therefore not useable in a Bayesian sensor fusion framework

If, for example, an object detection system is fooled by data outside of its training data distribution (Goodfellow et al., 2014; Nguyen et al., 2015a), the consequences for a robot acting on false, but high-confidence detections can be catastrophic

As the robot moves in its environment, the camera will observe the scene from different viewpoints, which poses both challenges and opportunities to a robotic vision system […] One of the biggest advantages robotic vision can draw from its embodiment is the potential to control the camera, move it, and change its viewpoint to improve its perception or gather additional information about the scene. This is in stark contrast to most computer vision scenarios […] As an extension of active vision, a robotic system could purposefully manipulate the scene to aid its perception

In his influential 1867 book on physiological optics, Von Helmholtz (1867) formulated the idea that humans use unconscious reasoning, inference or conclusion, when processing visual information. Since then, psychologists have devised various experiments to investigate these unconscious mechanisms (Goldstein and Brockmole, 2016), modernized Helmholtz’s original ideas (Rock, 1983), and reformulated them in the framework of Bayesian inference (Kersten et al., 2004).

The properties of model-based and deep-learned approaches can be measured along multiple dimensions, including the kind of representations used for reasoning, how generally applicable their solutions are, how robust they are in real-world settings, how efficiently they make use of data, and how computationally efficient they are during operation. Model-based approaches often rely on explicit models of objects and their shape, surface, and mass properties, and use these to predict and control motion through time. In deep learning, models are typically implicitly encoded via networks and their parameters. As a consequence, model-based approaches have wide applicability, since the physics underlying them are universal. However, at the same time, the parameters of these models are difficult to estimate from perception, resulting in rather brittle performance operating only in local basins of convergence. Deep learning, on the other hand, enables highly robust performance when trained on sufficiently large data sets that are representative of the operating regime of the system. However, the implicit models learned by current deep learning techniques do not have the general applicability of physics-based reasoning. Model-based approaches are significantly more data efficient, related to their smaller number of parameters. The optimizations required for model-based approaches can be performed efficiently, but the basin of convergence can be rather small. In contrast, deep-learned solutions are often very fast and can have very large basins of convergence. However, they do not perform well if applied in a regime outside the training data

Posted in: Robotics , Tagged: