# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 1 Introduction
### Previous Studies  
Previous studies related to wearable systems have developed systems suitable for the purpose of each study; however, details have been lacking regarding the placement method for the sensors of the wearable system for efficient data acquisition. [PERSON] et al. [3] developed a backpack LiDAR system comprising two 3D LiDARs for 3D modeling of underground parking in a GNSS-denied environment. [PERSON] et al. [11] developed a wearable mapping system using a hand-held laser scanner for 3D modeling of a historical site. [PERSON] et al. [10] performed data fusion for data acquired in the forest area using a backpack-type wearable system equipped with LiDAR and multiple optical cameras. [PERSON] et al. [13] developed a system to help visually impaired people move indoors using a wearable system composed of LiDAR and a camera. However, these studies did not provide specific criteria or reasons for the arrangement and design of their sensors and sensor systems. One previous study [14] related to this research indicated the method for LiDAR sensor arrangement within the backpack system; however, that study dealt only with LiDAR. Self-calibration of optical cameras and LiDAR sensors has been sufficiently verified through various previous studies. The calibration methodology for conventional and fish-eye lens cameras has been established through various studies [15; 16; 17; 18; 19; 20; 21; 22; 23; 24] from the 1960s to recent years. Furthermore, regarding LiDAR self-calibration, various methodologies have been proposed and verified through numerous studies [25; 26; 27; 28; 29; 30]. Therefore, the self-calibration of optical cameras and LiDARs can be performed reliably and accurately via the approaches of previous studies. Various studies have proposed multi-sensors system calibration approaches; however, most of the previous studies focused on the characteristics of the sensor without considering the platform used in the development of the system calibration approach. In other words, previous approaches mainly considered the correspondences between control features (points, lines, and planes) of sensor data, not the characteristics of the sensor arrangement according to the platform. The point-point correspondence-based approach has been the most commonly used methodology of system calibration. This approach allows stable calibration, because several matched points robustly limit the relative position of the sensors. Therefore, many previous studies used the point-point correspondence-based approach to conduct system calibration between an optical camera and a LiDAR, using the camera images and intensity images [31; 32; 33; 34; 35] or LiDAR point clouds [37; 38; 39; 40; 41; 42; 43; 44]. However, this approach has the disadvantage that the accuracy is dependent on the resolution of the data and the FOV overlap between the sensors [45; 46; 47; 48; 49]. The conventional point-plane correspondence-based approach is a calibration method for an optical camera and a LiDAR. The constraint condition of this approach is that LiDAR point clouds for a plane should be on the corresponding plane derived from the optical images. This approach has the advantage that it requires nothing but checkerboard calibration targets in the testbed configuration [50]. Thus, several studies have employed the conventional point-plane correspondence-based approach [51; 52; 53; 54; 55]. However, the approach has a disadvantage in that (i) the application is limited to a system consisting of an optical camera and a LiDAR with sufficient FOV overlap, and (ii) it requires an additional process for extracting 3D plane information from optical images. The line-plane and point-line correspondence-based approaches are generally used for the systems with 2D LiDAR and cameras. These approaches offer the advantage that the positional relationship between control features is strongly constrained, and they derive accurate results [56; 57]. Therefore, they have been utilized in various studies dealing with 2D LiDAR [56; 57; 58; 59; 60; 61; 62; 63; 64].