Calibrating Distance Sensors for Terrestrial Applications Without Groundtruth Information

Abstract

Abstract-This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move.The proposed calibration procedure exploits an approximated EM scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously.Tests in a controlled environment led to a reduction of the MSE of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

“…• Certain assumptions about the sensor movement and about the surrounding environment, in which the calibration process is shaped as joint parameters and state estimation, for example, lidar calibration from linear motion [8].…”

“…• Another strategy for substituting ground truth information with some other information is to implement appropriate sensor fusion strategies, i.e., combine redundant information from independent distance sensors. Such a strategy has been used in [8,9], where approximated Expectation Maximization (EM) procedures (in the former) and Markov chain Monte Carlo (MCMC) techniques under Bayesian frameworks (in the later) are used for joint parameter and state estimation combining information from lidars, odometry, and ultrasound sensors.…”

“…the distances estimated by the data-filtering algorithm. We analyse how the MSE ratio (31) depends on the standard deviation σ θ of the sensor-to-landmark angle measurement error in (6), and the standard deviation σ s of the sensor position evolution uncertainty in (8).…”

“…In other words, solving (21) is computationally feasible since all the required information is available while solving (18) is not. However, solving (21) leads to solving an approximate model of the intersection problem which will indeed result in a biased estimator, since it corresponds to solving the system of equations that is obtained after substituting x k and y k from (8) into (17), i.e., sin…”

Energy efficient TDMA access technique for surveillance in terrestrial sensor networks

“…Prosedürlerini sadece genel bir mesafe algılayıcı parametrelerini tahmin etmek için değil, aynı zamanda robotik uygulamalarda en yaygın kullanılan algılayıcıları entegre etmek ve algılayıcıların içsel parametrelerini öğrenmek için tasarlamışlardır. Yaptıkları testlerin, doğrusal hareketler varsayımını ihlal etmeye karşı yüksek sağlamlık gösterdiğini vurgulamışlardır [20].…”