Tag Archives: Robot Localization

Testbed for comparisons of different UWB sensors applied to localization

A. R. Jiménez Ruiz and F. Seco Granja, “Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis,” in IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 8, pp. 2106-2117, Aug. 2017.DOI: 10.1109/TIM.2017.2681398.

Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.

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.