# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
###### Abstract  
The use of spherical cameras for mapping purposes is a common application in surveying. Very expensive and high-quality cameras are used for surveying purposes and are supplemented by systems for determining their position. Cheap cameras, in most cases, only complement laser scanners, and the images are then used to color the laser point cloud. This article investigates the use of action cameras in combination with low-cost GNSS (Global Navigation Satellite System) equipment. The research involves the development of a methodology and software for georeferencing spherical images, created by the kinematic method, using GNSS RTK (Real-Time Kinematics) or PPK (Post-Processing Kinematics) coordinates. Testing was carried out in two case studies where the environment surveyed had varying properties. Considering that the images from the low-cost 360 camera are of lower quality, an artificial intelligence tool was used to improve the quality of the images. The point clouds from a low-cost device are compared with more accurate methods. One of them is the SLAM (Simultaneous Localization and Mapping) method with the Faro Orbis device. The results in this work show sufficient accuracy and data quality for mapping purposes. Due to the very low price of the low-cost device used in this work, it is very easy to extend this method to practice. spherical camera; SfM-MVS; low-cost mapping; GNSS; machine learning Article  
[PERSON], Pa  
GNSS/IMU or GNSS/INS systems for georeferencing data [2; 3]. With the rise of Google Street View, panoramic photography, known as Street View, became very popular. To create these spherical images, cameras created by several sensors with low-distortion lenses were used [4]. This principle of using multiple sensors is used in most measurement cameras such as the Ladybug5 [5; 6] or even in cameras that are part of a multisensory mapping device [7]. Subsequently, in the second decade of the 20 th century, personal mapping laser systems (PLSs) appeared, carried by operators or in the form of backpacks, such as, most famously, the Leica Pegasus [8]. Smaller PLSs are mainly used in enclosed spaces where there is no GNSS signal. In this case, SLAM technology is used to determine the position of the device and its trajectory. In most cases, this technology uses a laser scanner to map the surroundings while the operator is moving [9; 10]. Spherical action cameras have a very similar design; spherical photos are created using two sensors with wide-angle lenses [11]. These cameras have already been tested for indoor modeling purposes, but also for exterior measurement [12; 13; 14]. The articles report a standard deviation of static imaging up to 8 cm. However, in good lighting conditions, in a nonhomogeneous environment, the resulting spatial accuracy of tens of mm can be achieved. The precision in the kinematic measurement of indoor objects varies between 2 and 10 cm, depending on the type of camera and the quality of its output [15; 16]. Compared to professional cameras, these low-cost action cameras, due to their design, have more problems with the blurring of images, lower image quality, and a non-standard model of distortion of the panoramic image, and when shooting in poorly lighted places. The problem of low-resolution images can be reduced by using artificial intelligence tools [17] and a deep convolutional neural network (DCNN) [18]. The Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) [19; 20] architecture, which aims to predict high-resolution images from corresponding low-resolution images, finds applications in photogrammetry. The Structure-from-Motion method [21], in combination with the Multi-View Stereo (SfM-MVS), is suitable for the reconstruction of 3D environments from spherical images. This method is low-cost and easy to use; the positions and orientations of the cameras are determined automatically by iterative computation.