Category Archives: Mobile Robot Localization

Improving orientation estimation in a mobile robot for doing better odometry

M.T. Sabet, H.R. Mohammadi Daniali, A.R. Fathi, E. Alizadeh, Experimental analysis of a low-cost dead reckoning navigation system for a land vehicle using a robust AHRS, Robotics and Autonomous Systems, Volume 95, 2017, Pages 37-51, DOI: 10.1016/j.robot.2017.05.010.

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue especially when the localization sensors such as GPS are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework an Extended Kalman Filter (EKF) is utilized for estimation of attitude, heading and gyroscope sensor bias using a low-cost MEMS inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study using the proposed attitude and heading reference system (AHRS) and an odometer sensor, a low-cost aided DR navigation system has been designed. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for a land vehicle. The results indicate that the roll, pitch and heading are estimated by mean value errors about 0.83%, 0.68% and 1.13%, respectively. Moreover, they indicate that a relative navigation error about 3% of the traveling distance can be achieved using the developed approach in during GPS outages.

Identification of beacons for localization by using LEDs with light patterns as IDs

G. Simon, G. Zachár and G. Vakulya, Lookup: Robust and Accurate Indoor Localization Using Visible Light Communication, IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 9, pp. 2337-2348, DOI: 10.1109/TIM.2017.2707878.

A novel indoor localization system is presented, where LED beacons are utilized to determine the position of the target sensor, including a camera, an inclinometer, and a magnetometer. The beacons, which can be a part of the existing lighting infrastructure, transmit their identifiers for long distances using visible light communication techniques. The sensor is able to sense and detect the high-frequency (flicker free) code by properly undersampling the transmitted signal. The localization is performed using novel geometric and consensus-based techniques, which tolerate well measurement inaccuracies and sporadic outliers. The performance of the system is analyzed using simulations and real measurements. According to large-scale tests in realistic environments, the accuracy of the proposed system is in the low decimeter range.

A novel particle filter algorithm with an adaptive number of particles, and a curious and interesting table I about the pros and cons of different sensors

T. de J. Mateo Sanguino and F. Ponce Gómez, “Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering,” in IEEE/ASME Transactions on Mechatronics, vol. 21, no. 6, pp. 2793-2804, Dec. 2016.DOI: 10.1109/TMECH.2016.2531629.

The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter – DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.

Globally optimal ICP

J. Yang, H. Li, D. Campbell and Y. Jia, “Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, pp. 2241-2254, Nov. 1 2016. DOI: 10.1109/TPAMI.2015.2513405.

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the $L_2$ error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space $SE(3)$ . By exploiting the special structure of $SE(3)$ geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

Combination of several mobile robot localization methods in order to achieve high accuracy in industrial environments, with interesting figures for current localization accuracy achievable by standard solutions

Goran Vasiljevi, Damjan Mikli, Ivica Draganjac, Zdenko Kovai, Paolo Lista, High-accuracy vehicle localization for autonomous warehousing, Robotics and Computer-Integrated Manufacturing, Volume 42, December 2016, Pages 1-16, ISSN 0736-5845, DOI: 10.1016/j.rcim.2016.05.001.

The research presented in this paper aims to bridge the gap between the latest scientific advances in autonomous vehicle localization and the industrial state of the art in autonomous warehousing. Notwithstanding great scientific progress in the past decades, industrial autonomous warehousing systems still rely on external infrastructure for obtaining their precise location. This approach increases warehouse installation costs and decreases system reliability, as it is sensitive to measurement outliers and the external localization infrastructure can get dirty or damaged. Several approaches, well studied in scientific literature, are capable of determining vehicle position based only on information provided by on board sensors, most commonly wheel encoders and laser scanners. However, scientific results published to date either do not provide sufficient accuracy for industrial applications, or have not been extensively tested in realistic, industrial-like operating conditions. In this paper, we combine several well established algorithms into a high-precision localization pipeline, capable of computing the pose of an autonomous forklift to sub-centimeter precision. The algorithms use only odometry information from wheel encoders and range readings from an on board laser scanner. The effectiveness of the proposed solution is evaluated by an extensive experiment that lasted for several days, and was performed in a realistic industrial-like environment.

Integration of the ICP algorithm with a Kalman filter to improve relative localization, with a good state-of-the-art of ICP algorithms

F. Aghili and C. Y. Su, “Robust Relative Navigation by Integration of ICP and Adaptive Kalman Filter Using Laser Scanner and IMU,” in IEEE/ASME Transactions on Mechatronics, vol. 21, no. 4, pp. 2015-2026, Aug. 2016.DOI: 10.1109/TMECH.2016.2547905.

This paper presents a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction, while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic, and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose-tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center of mass. The robustness and accuracy of the relative navigation algorithm is demonstrated through a hardware-in-the loop simulation setting, in which actual vision data for the relative navigation are generated by a laser range finder scanning a spacecraft mockup attached to a robotic motion simulator.

Interesting approach to PF-based localization and active localization when the map contains semantic information

Nikolay Atanasov, Menglong Zhu, Kostas Daniilidis, and George J. Pappas, Localization from semantic observations via the matrix permanent, The International Journal of Robotics Research January–March 2016 35: 73-99, first published on October 6, 2015, DOI: 10.1177/0278364915596589.

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization.

Using the Bingham distribution of probability, which is defined on a d-dimensional sphere to be antipodally symmetric, to address the problem of angle periodicity in [0,2pi] when estimating orientation in a recursive filter

Gilitschenski, I.; Kurz, G.; Julier, S.J.; Hanebeck, U.D., Unscented Orientation Estimation Based on the Bingham Distribution, in Automatic Control, IEEE Transactions on , vol.61, no.1, pp.172-177, Jan. 2016, DOI: 10.1109/TAC.2015.2423831.

In this work, we develop a recursive filter to estimate orientation in 3D, represented by quaternions, using directional distributions. Many closed-form orientation estimation algorithms are based on traditional nonlinear filtering techniques, such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). These approaches assume the uncertainties in the system state and measurements to be Gaussian-distributed. However, Gaussians cannot account for the periodic nature of the manifold of orientations and thus small angular errors have to be assumed and ad hoc fixes must be used. In this work, we develop computationally efficient recursive estimators that use the Bingham distribution. This distribution is defined on the hypersphere and is inherently more suitable for periodic problems. As a result, these algorithms are able to consistently estimate orientation even in the presence of large angular errors. Furthermore, handling of nontrivial system functions is performed using an entirely deterministic method which avoids any random sampling. A scheme reminiscent of the UKF is proposed for the nonlinear manifold of orientations. It is the first deterministic sampling scheme that truly reflects the nonlinear manifold of orientations.

Comparison of EKF and UKF for robot localization and a method of selection of a subset of the available sonar sensors

Luigi D’Alfonso, Walter Lucia, Pietro Muraca, Paolo Pugliese, Mobile robot localization via EKF and UKF: A comparison based on real data, Robotics and Autonomous Systems, Volume 74, Part A, December 2015, Pages 122-127, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.07.007.

In this work we compare the performance of two well known filters for nonlinear models, the Extended Kalman Filter and the Unscented Kalman Filter, in estimating the position and orientation of a mobile robot. The two filters fuse the measurements taken by ultrasonic sensors located onboard the robot. The experimental results on real data show a substantial equivalence of the two filters, although in principle the approximating properties of the UKF are much better. A switching sensors activation policy is also devised, which allows to obtain an accurate estimate of the robot state using only a fraction of the available sensors, with a relevant saving of battery power.

One of the first thorough studies of Monte Carlo Localization with line-segment maps

Biswajit Sarkar, Surojit Saha, Prabir K. Pal, A novel method for computation of importance weights in Monte Carlo localization on line segment-based maps, Robotics and Autonomous Systems, Volume 74, Part A, December 2015, Pages 51-65, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.07.001.

Monte Carlo localization is a powerful and popular approach in mobile robot localization. Line segment-based maps provide a compact and scalable representation of indoor environments for mobile robot navigation. But Monte Carlo localization has seldom been studied in the context of line segment-based maps. A key step of the approach–and one that can endow it with or rob it of the attributes of accuracy, robustness and efficiency–is the computation of the so called importance weight associated with each particle. In this paper, we propose a new method for the computation of importance weights on maps represented with line segments, and extensively study its performance in pose tracking. We also compare our method with three other methods reported in the literature and present the results and insights thus gathered. The comparative study, conducted using both simulated and real data, on maps built from real data available in the public domain clearly establish that the proposed method is more accurate, robust and efficient than the other methods.