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Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.
Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.
This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.
