An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system

Correspondence rejection by trilateration for 3D point cloud registration

Recent years have shown increases in virtual 3D perception and applications, many of these applications require 3D model reconstruction from high quality LIDAR scans. High quality 3D models may be acquired from a collection of overlapping LIDAR scans which need to be registered or aligned to a common coordinate system. This paper investigates the use of a novel implementation of trilateration for correspondence rejection in highly accurate 3D point cloud registration. It is shown that from a synthesized correspondence set of size 100 containing 85% outliers, all or most of the remaining 15% inliers can be retrieved. The trilateration problem is solved for all 4-combinations of correspondence elements from which the true correspondence subsets are easily identifiable. It is also shown that this method's performance may be greatly affected by noisy distance measurements, however the method works well for distance measurements typically acquired by LIDAR systems. Lastly, unnecessarily large sizes of correspondence sets can quickly make the method computationally expensive if all combination subsets require to be evaluated