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State Estimation for Mobile Robotics
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State Estimation for Mobile Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 16011

Special Issue Editors

Dr. Geoff Fink

E-Mail Website
Guest Editor
Dynamic Legged Systems lab, Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy
Interests: state estimation; computer vision; visual serving; perception
Dr. Claudio Semini

E-Mail Website
Guest Editor
Dynamic Legged Systems lab, Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy
Interests: legged robots; locomotion; quadruped robot
Dr. Giuseppe Loianno

E-Mail Website
Guest Editor
Tandon School of Engineering, New York University, New York, NY 11201, USA
Interests: Robotics; MAVs; Vision; sensor fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Laurent Kneip

E-Mail Website
Guest Editor
School of Information Science Technology, ShanghaiTech University, Shanghai 201210, China
Interests: visual localization; visual SLAM; structure from motion; algebraic geometry; state estimation; sensor fusion; deep learning
Dr. Guoquan Huang

E-Mail Website
Guest Editor
Department of Mechanical Engineering University of Delaware 208 Spencer Lab Newark, DE 19716, USA
Interests: SLAM; VINS; object tracking; multi-robot cooperative localization; sensor calibration; 3D scene understanding; spatial perception and cognition

Special Issue Information

Dear Colleagues,

Robotic literature is often focused on advanced control strategies, and state estimation is often an afterthought when moving from simulation to experiment. This leads to many robotic platforms not being able to achieve their full potential. Typically, for a mobile robot, the state will include the position and velocity. Furthermore, it can include information such as joint forces and torques, center of masses, or even 3D maps. State estimation is the problem of estimating this state from sensor data and models. For most state estimation problems, there are no sensors that can directly measure them, and the sensors that can partially measure them are corrupted with noise.

This Special Issue addresses all types of state estimation for mobile robotics.

Dr. Geoff Fink
Dr. Claudio Semini
Dr. Giuseppe Loianno
Dr. Laurent Kneip
Dr. Guoquan Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • State estimation
  • Nonlinear observers, sensor fusion, Kalman filtering
  • Localization, mapping, and SLAM
  • Robot modeling, parameter estimation, sensor calibration
  • Datasets for robotic state estimation
  • Modeling and simulation
  • Mobile robots, autonomous vehicles
  • Real-time applications
  • Embedded hardware
  • Novel sensors
  • Multi-modal State Estimation

Published Papers (5 papers)

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Research

14 pages, 4160 KiB  
Article
Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization
by Yifu Wang, Jiaqi Yang, Xin Peng, Peng Wu, Ling Gao, Kun Huang, Jiaben Chen and Laurent Kneip
Sensors 2022, 22(15), 5687; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155687 - 29 Jul 2022
Cited by 13 | Viewed by 1917
Abstract
We present a new solution to tracking and mapping with an event camera. The motion of the camera contains both rotation and translation displacements in the plane, and the displacements happen in an arbitrarily structured environment. As a result, the image matching may [...] Read more.
We present a new solution to tracking and mapping with an event camera. The motion of the camera contains both rotation and translation displacements in the plane, and the displacements happen in an arbitrarily structured environment. As a result, the image matching may no longer be represented by a low-dimensional homographic warping, thus complicating an application of the commonly used Image of Warped Events (IWE). We introduce a new solution to this problem by performing contrast maximization in 3D. The 3D location of the rays cast for each event is smoothly varied as a function of a continuous-time motion parametrization, and the optimal parameters are found by maximizing the contrast in a volumetric ray density field. Our method thus performs joint optimization over motion and structure. The practical validity of our approach is supported by an application to AGV motion estimation and 3D reconstruction with a single vehicle-mounted event camera. The method approaches the performance obtained with regular cameras and eventually outperforms in challenging visual conditions. Full article
(This article belongs to the Special Issue State Estimation for Mobile Robotics)
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14 pages, 3609 KiB  
Article
On Slip Detection for Quadruped Robots
by Ylenia Nisticò, Shamel Fahmi, Lucia Pallottino, Claudio Semini and Geoff Fink
Sensors 2022, 22(8), 2967; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082967 - 13 Apr 2022
Cited by 4 | Viewed by 3138
Abstract
Legged robots are meant to autonomously navigate unstructured environments for applications like search and rescue, inspection, or maintenance. In autonomous navigation, a close relationship between locomotion and perception is crucial; the robot has to perceive the environment and detect any change in order [...] Read more.
Legged robots are meant to autonomously navigate unstructured environments for applications like search and rescue, inspection, or maintenance. In autonomous navigation, a close relationship between locomotion and perception is crucial; the robot has to perceive the environment and detect any change in order to autonomously make decisions based on what it perceived. One main challenge in autonomous navigation for legged robots is locomotion over unstructured terrains. In particular, when the ground is slippery, common control techniques and state estimation algorithms may not be effective, because the ground is commonly assumed to be non-slippery. This paper addresses the problem of slip detection, a first fundamental step to implement appropriate control strategies and perform dynamic whole-body locomotion. We propose a slip detection approach, which is independent of the gait type and the estimation of the position and velocity of the robot in an inertial frame, that is usually prone to drift problems. To the best of our knowledge, this is the first approach of a quadruped robot slip detector that can detect more than one foot slippage at the same time, relying on the estimation of measurements expressed in a non-inertial frame. We validate the approach on the 90 kg Hydraulically actuated Quadruped robot (HyQ) from the Istituto Italiano di Tecnologia (IIT), and we compare it against a state-of-the-art slip detection algorithm. Full article
(This article belongs to the Special Issue State Estimation for Mobile Robotics)
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Graphical abstract

19 pages, 2073 KiB  
Article
Auto-Exposure Algorithm for Enhanced Mobile Robot Localization in Challenging Light Conditions
by Marc-André Bégin and Ian Hunter
Sensors 2022, 22(3), 835; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030835 - 22 Jan 2022
Cited by 4 | Viewed by 4478
Abstract
The success of robot localization based on visual odometry (VO) largely depends on the quality of the acquired images. In challenging light conditions, specialized auto-exposure (AE) algorithms that purposely select camera exposure time and gain to maximize the image information can therefore greatly [...] Read more.
The success of robot localization based on visual odometry (VO) largely depends on the quality of the acquired images. In challenging light conditions, specialized auto-exposure (AE) algorithms that purposely select camera exposure time and gain to maximize the image information can therefore greatly improve localization performance. In this work, an AE algorithm is introduced which, unlike existing algorithms, fully leverages the camera’s photometric response function to accurately predict the optimal exposure of future frames. It also features feedback that compensates for prediction inaccuracies due to image saturation and explicitly balances motion blur and image noise effects. For validation, stereo cameras mounted on a custom-built motion table allow different AE algorithms to be benchmarked on the same repeated reference trajectory using the stereo implementation of ORB-SLAM3. Experimental evidence shows that (1) the gradient information metric appropriately serves as a proxy of indirect/feature-based VO performance; (2) the proposed prediction model based on simulated exposure changes is more accurate than using γ transformations; and (3) the overall accuracy of the estimated trajectory achieved using the proposed algorithm equals or surpasses classic exposure control approaches. The source code of the algorithm and all datasets used in this work are shared openly with the robotics community. Full article
(This article belongs to the Special Issue State Estimation for Mobile Robotics)
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18 pages, 3462 KiB  
Article
Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results
by Sindre Fossen and Thor I. Fossen
Sensors 2021, 21(23), 7910; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237910 - 27 Nov 2021
Cited by 10 | Viewed by 2866
Abstract
Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot [...] Read more.
Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems. Full article
(This article belongs to the Special Issue State Estimation for Mobile Robotics)
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17 pages, 985 KiB  
Article
Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation
by Rafael Carbonell, Ángel Cuenca, Vicente Casanova, Ricardo Pizá and Julián J. Salt Llobregat
Sensors 2021, 21(22), 7557; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227557 - 13 Nov 2021
Cited by 6 | Viewed by 2042
Abstract
In this paper, a two-wheel drive unmanned ground vehicle (UGV) path-following motion control is proposed. The UGV is equipped with encoders to sense angular velocities and a beacon system which provides position and orientation data. Whereas velocities can be sampled at a fast [...] Read more.
In this paper, a two-wheel drive unmanned ground vehicle (UGV) path-following motion control is proposed. The UGV is equipped with encoders to sense angular velocities and a beacon system which provides position and orientation data. Whereas velocities can be sampled at a fast rate, position and orientation can only be sensed at a slower rate. Designing a dynamic controller at this slower rate implies not reaching the desired control requirements, and hence, the UGV is not able to follow the predefined path. The use of dual-rate extended Kalman filtering techniques enables the estimation of the fast-rate non-available position and orientation measurements. As a result, a fast-rate dynamic controller can be designed, which is provided with the fast-rate estimates to generate the control signal. The fast-rate controller is able to achieve a satisfactory path following, outperforming the slow-rate counterpart. Additionally, the dual-rate extended Kalman filter (DREKF) is fit for dealing with non-linear dynamics of the vehicle and possible Gaussian-like modeling and measurement uncertainties. A Simscape Multibody™ (Matlab®/Simulink) model has been developed for a realistic simulation, considering the contact forces between the wheels and the ground, not included in the kinematic and dynamic UGV representation. Non-linear behavior of the motors and limited resolution of the encoders have also been included in the model for a more accurate simulation of the real vehicle. The simulation model has been experimentally validated from the real process. Simulation results reveal the benefits of the control solution. Full article
(This article belongs to the Special Issue State Estimation for Mobile Robotics)
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