We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploying special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyse their statistical performance both in simulation and with field tests, reporting thus the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observing that in field tests the approach can lead to a ten-fold improvement in the accuracy of the raw measurements.

Background Epidemiological studies have associated plasma galectin-4 (Gal-4) levels with prevalent and incident diabetes, and with increased risk of coronary artery disease, but data regarding possible associations with stroke are lacking. Methods We tested Gal-4 association with prevalent stroke in a population-based cohort, and whether Gal-4 increases after ischemic stroke in mice fed a high-fat diet (HFD). Results Gal-4 was higher in subjects with compared to without prevalent ischemic stroke, and was associated with prevalent ischemic stroke (odds ratio 1.52; 95% confidence interval 1.01-2.30; p=0.048) adjusted for age, sex and covariates of cardiometabolic health. Plasma Gal-4 increased after stroke in both controls and HFD-fed mice, but HFD exposure was devoid of impact in Gal-4 levels. Conclusions This study demonstrates higher plasma Gal-4 levels in both a stroke mouse model and individuals with prevalent stroke.