# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 1 Introduction  
Over the past few years, significant developments of laser scanning technology have increased the feasibility of acquiring large amounts of accurate geometric 3D data. The demand for 3D observation has increased as well with the development of automatic digital image analysis, namely artificial intelligence. In this context, laser scanners have become a fundamental means to acquire 3D information in a manner that is effective enough to satisfy the growing demand in this field [1]. This trend is particularly relevant in civil engineering, robotics, and computer vision, due to those fields' high usage of laser scanners as routine measurement techniques for applications such as 3D modeling and mapping [2, 3], efficient building management [4, 5], and the transformation of structural health monitoring [6, 7]. Extending the application of laser scanners from geomatics to these domains has resulted in the emergence of new areas for potential implementation, which requires the developmentof more efficient and accurate acquisition systems [8]. More recently, Light Detection and Ranging (LiDAR) sensors have become more portable, compact, and readily available to be extended to mobile applications. The advancement of modern technology has expanded the use of LiDAR sensors to a variety of applications including mobile mapping, surveying, and autonomous vehicles. In particular, Multi-Beam LiDAR (MBL) is one of the most extensively used and favored sensors for mobile applications, because the sensors are relatively lightweight, compact, and cheap. MBL, manufactured by Velodyne LiDAR, has been widely used both in research and the industrial field [9; 10; 11; 12; 13; 14; 15; 16; 17]. Particularly, backpack-based 3D mapping using Velodyne LiDAR has been a popular mobile application. Leica Pegasus [18], Viametris bMS3D [19], GreenValley LiBackpack [20], and Geexel Heron [21] are examples of the commercial backpack-based mobile mapping system. These solutions use two Velodyne VLP-16 units to perform odometry for georeferencing and 3D mobile mapping including Simultaneous Localization and Mapping (SLAM). Along with the growing need for 3D information, these commercial backpack-based mapping systems were released to match the demand for quick data acquisition and fieldwork planning; however, there are drawbacks that require further improvement in terms of accuracy and cost [22]. In this context, developing an accurate and affordable backpack-based mapping system is still an interest in this field. For successful and effective mobile LiDAR scanning, two fundamental issues need to be addressed: georeferencing and sensor calibration. Georeferencing is the conversion of a local coordinate system to one global coordinate system combining all point clouds into the same coordinate system. Sensor calibration removes systematic errors inherent to sensors and can accomplish quality assurance to maximize the accuracy of observation. These two key processes are not independent, which means that the more accurate the sensor system that is built, the more accurate georeferencing becomes, so that the overall accuracy of surveying can be improved. Therefore, establishing an accurate mobile LiDAR sensor system is the most significant step. Even though MBL provides a cost-efficient and portable option, their observation accuracy is lower than conventional Terrestrial Laser Scanners (TLS) in general. They include systematic errors, which can affect the overall accuracy of the scanned data. Since each mechanically designed laser measures the range by time-of-flight system and encoder angle, the point cloud inevitably contains systematic errors in range and angle measurements with respect to each laser. These systematic errors can cause translations and rotations in the point cloud data. As a result, for precise mobile mapping and surveying, the overall accuracy of the point cloud data needs to be improved [23].