## 2 Literature Review  
### Indoor Robot Localization Techniques  
Robot localization is the process of estimating the position and orientation of a mobile robot with respect to the environment, which is an important task for mobile robots as it is essential for path planning [15]. Many technologies are used to localize mobile robots, and they are usually divided into relative positioning and absolute positioning. The relative (local) localization uses internal sensors (such as wheel encoders, inertial sensors, and digital compasses) to estimate the robot's pose [16]. The absolute (global) localization uses external sensors (such as cameras and lasers) that enable direction calculation of the location with respect to the environment. The relative localization of the robot is estimated based on the information obtained through the integration of different sensors. The integration is initiated at the starting position and continuously updated throughout time. Relative position measurement techniques are widely used because of their low cost and faster pose estimation. However, relative positioning cannot be used to measure long distances due to errors, such as wheel slippage and other unbounded errors, which continue to accumulate with increases in the distance traveled by the robot [15]. Wheel odometer-based localization is a navigation method for relative positioning techniques in the mobile robot field, known as dead reckoning, which uses a rotary encoder that measures the rotation of the wheels to calculate the pose of the robot. It is very simple, inexpensive, and has good accuracy over a short range and short term, but it incurs a high level of uncertainty in cumulative position estimation over time due to wheel-slip, changes in wheel dimensions, and misalignment. The level of uncertainty could be mitigated by combining multiple sensors optimally and generating better position estimation through Kalman filter fusion techniques [17; 18]. The Inertial navigation systems (INSs) can also be utilized to estimate the pose of a robot with respect to the starting point. They typically include IMUs, which are equipped with accelerometers, gyroscopes, and magnetometers to calculate the position, direction, and velocity of a moving object. The advantages of the inertia sensors include simple implementation, lower prices, and utilization without prior knowledge about the environment [19]. Even though INS-based approaches do not require external references for positioning, they are vulnerable to cumulative errors that can lead to large localization errors in a short period of time. This is because the mathematical integral of the acceleration with respect to time should be calculated to estimate the pose. The small errors in acceleration and velocity accumulate over the integral calculation and produce increasingly inaccurate position estimates. The magnetometer sensor in the IMU could provide the robot's absolute heading by measuring the earth's magnetic field; however, the earth's magnetic field is often distorted near metal objects, which makes it inappropriate for indoor applications [20]. Therefore, any type of inertial sensor is not appropriate for long-term position estimation. Unlike relative localization, absolute localization does not suffer from error accumulation because the calculation of the current pose does not depend on the previous poses. However, the measurement of small movements using external sensors may not be as accurate as that with internal sensors [19]. One of the most popular external sensors for absolute localization is GPS (Global Positioning System). Its performance is negatively affected by the canopy, bad weather, and building structures blocking the signal strength to the GPS receiver [21]. This aspect makes GPS unreliable for the indoor localization of objects, which is the focus of this paper. Video cameras are also utilized for the absolute localization of mobile robots. Camera-based methods can achieve relatively accurate positioning with a low cost, low weight, and low power consumption.