## 1 Introduction
be customized to meet specific challenges and requirements, enabling innovative camera placement scenarios. Different camera types are designed to capture specific portions of the electromagnetic spectrum. While red, green, and blue (RGB) cameras are the most common and affordable type used, specialized cameras--such as hyperspectral, multi-spectral, and infrared--are employed for specific applications like material classification or thermal measurement [14]. Calibrating spectral measurements is discussed in radiometric photogrammetry, which deals with the complexities of light emission, reflection, and absorption, alongside the technical challenges of calibrating devices that measure various spectral bands [15]. This field is crucial for ensuring the accuracy of data collected by MMS, particularly when working with spectral information. Another key measurement method used in MMSs is based on laser technology [16], which was first discovered in 1960. This discovery was soon followed by the invention of light detection and ranging (LiDAR) in 1962. Within a decade, LiDAR found significant real-world applications; notably, it was utilized during the Apollo mission (15-17) in 1971-1972 for planetary measurements and estimating the spacecraft's distance from the Moon [17]. Over the next two decades, increasingly sophisticated LiDAR systems emerged for scientific and engineering applications. Despite their high costs, LiDAR sensors revolutionized MMS technologies by providing precise, high-quality 3D data of the environment [18]. This breakthrough enabled the development of complex MMS setups that addressed specific needs and paved the way for the upcoming digital revolution [19]. The evolution of MMSs can be divided into several key phases: the discovery of pulsed laser technology, the advancement of digital photogrammetry and computer vision techniques, maturation of GNSS and LiDAR sensors, and breakthroughs in computational resources and machine-learning methods. Together, these advancements have enabled MMSs to become powerful tools for capturing and analyzing spatial data, transforming fields such as urban planning, environmental monitoring, and infrastructure management. The integration of LiDAR into MMSs has profoundly impacted the geospatial industry, allowing for detailed modeling of objects and landscapes and expanding possibilities for future innovations. One of the key direct outputs of employing MMS is its ability to provide location awareness through the fusion of diverse sensors, a fundamental concept required by many modern industries and technologies, such as robotics and autonomous vehicles [20]. Location awareness refers to a computer system's ability to understand the geometric complexities of its operating environment. Even when the sensors and related software are not explicitly labeled as part of an MMS, these technologies often function as critical subsystems within larger, more complex systems. Consequently, advancements in the MMS field--such as data standardization, new sensor development, innovative calibration techniques, sensor fusion, and efficient computational hardware--can significantly benefit a wide range of applications. These improvements enhance the precision, speed, and consistency of systems reliant on spatial awareness, driving progress in sectors such as robotics, navigation, and automation. The development of low-cost sensors and miniaturized platforms has democratized access to MMS technology, making it available for a broader range of users, from researchers to everyday consumers. One notable trend is the miniaturized MMSs integrated into consumer mobile phones. This innovation has attracted significant investments and is expected to rapidly bring complex MMS technology into the hands of millions of everyday users. As this trend continues, it promises to revolutionize how individuals interact with spatial data, opening new possibilities in navigation, augmented reality, and personal mapping applications.