## 2 Literature Review
### Indoor Robot Localization Techniques
Visual sensor data from cameras provide rich information essential for localization, mapping, obstacle detection, and path planning without the need for merging other sensors. However, cameras suffer from the influence of lighting conditions, especially in an outdoor environment, and distortion due to fast movement, in addition to its view being narrow [21]. As another approach for absolute localization, a laser range finder measures relative distances between a robot and objects in its surroundings with the time of flight of the pulse. The distances can be used to generate a metric map or to determine the location of the robot within the map of the known environment. Landmark-based absolute localization attaches landmarks as fixed features that mobile robots can directly match with known locations. It is classified into an artificial landmark or natural landmark. Artificial landmarks are objects or shapes that are placed in the environment for the sole purpose of robot localization, while natural landmarks are features that already exist in the environment, which can be used for localization. The artificial landmarks can be geometric shapes or objects and other additional information in the form of bar-codes [22]. Many approaches explicitly mark the indoor environment by laying down magnetic lines that a robot can follow [23] or placing QR-coded markers that visually represent known locations [7]. Although these approaches can be highly reliable in a static environment, those markers cannot cope with changes made to the initial setting. The relocation of common items, such as furniture and large electronic appliances, can block the line-of-sight of the mark or block signals prohibiting positioning. Many localization techniques were then proposed to enhance the accuracy and robustness based on multiple sensor fusion and a probabilistic approach. Multi-sensor fusion combines multiple sensor data, such as an RGB camera, RGBD camera, LiDAR, radar, and other sensors that measure relative and absolute localization [24]. The most well-developed probabilistic approaches are based on a Bayes Filter. From EKF (Extended Kalman Filter) to a particle filter, it recursively updates the conditional probability distribution over the state space of the robot to estimate its pose. In particular, representing the posterior distribution based on a set of samples or particles, the particle filter-based approaches [25], also known as MCL methods, are most widely used for robot localization since they could deal with nonlinear and non-Gaussian problems, and almost arbitrary distributions can be represented. However, it requires a sufficiently large number of particles to cover the state space to generate pose estimates with acceptable accuracy and success rates. Hence, MCL methods may perform poorly if the size of the sample set is small in a large environment and the complexity, and memory cost of this kind of method will grow significantly as the state space expands [26]. Furthermore, this approach relies on the consistency between the given map and real environment. Therefore, it becomes important that the robot be able to deal with the changes in the environment over time [27]. Especially, the construction environment keeps changing over time, and various objects in the construction environment should be dynamically updated in the map. For autonomous robot systems to effectively operate based on a building environment, they must be able to refer to or derive a semantic model of the environment [28]. Some researchers have made it possible for the robot to access project information contained in a Building Information Model (BIM), which is a comprehensive database of a built environment [29].