# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 1 Introduction  
Since the 21 st century, with the rapid development of microelectronics technology, artificial intelligence technology, new materials technology, and computer technology, simultaneous localization and mapping (SLAM) technology has shown great application potential in many real scenarios [1, 2, 3]. For example, in the field of automatic driving, the SLAM system constructs a high-precision environment map and locates vehicle position in real time by fusing lidar, vision, and inertial measurement unit (IMU) data, providing a key basis for path planning and obstacle avoidance decisions [4, 5]. Similarly, for warehousing and the logistics robot, SLAM technology helps the robot accurately locate items in the dynamic shelf environment and improve the efficiency of sorting, handling, and inventory management [5, 6]. In addition, SLAM is also widely used in UAV patrol, augmented reality (AR), and service robots [7, 8, 9, 10]. Simultaneous Localization and Mapping (SLAM) technology [11, 12] is a key technology for the mobile robot to achieve environmental perception and self-localization. SLAM technology can be described as the robot moving from the unknown position in the unknown environment, positioning itself based on pose estimation and sensor observation information during the movement process, and establishingan environmental map. Therefore, stable, reliable, and high-precision pose estimation technology is one of the key technologies for achieving autonomous robot movement and practical applications [13; 14]. The pose estimation is the process of predicting the robot's position and posture in the unknown environment based on the certain algorithm and combined with sensor observation information. According to its principles, the pose estimation technique can be divided into relative pose estimation and absolute pose estimation [15]. The relative pose estimation method requires the initial pose of the robot to be known, and then utilizes sensor information and observed relative motion states to indirectly estimate the current pose of the robot. Absolute pose estimation does not require the initial pose information of the robot, and can estimate the pose state of the mobile robot solely based on sensor information, with relatively stable estimation accuracy [16]. Therefore, this paper mainly studies the absolute pose estimation of the mobile robot based on map-matching positioning. In practical applications, the motion model and observation model in SLAM is usually the nonlinear system, and the Extended Kalman Filter SLAM (EKF-SLAM) [13; 17; 18; 19] is one of the commonly used nonlinear pose estimation algorithms in SLAM. EKF-SLAM applies Taylor series to linearly approximate nonlinear systems, resulting in significant linearization errors. Therefore, some scholars have proposed the Unscented Kalman Filter (UKF-SLAM) algorithm [20; 21; 22], which directly approximates the probability density distribution of nonlinear functions and uses the sigma point set to approximate the posterior probability density of the target state. However, the UKF-SLAM algorithm cannot guarantee the positive definiteness of the covariance matrix, which can lead to a decrease in the filtering accuracy of the UKF-SLAM algorithm. Researchers have proposed the Square Root Unscented Kalman Filter SLAM (SRUKF-SLAM) algorithm [23; 24], which uses the square root matrix of covariance instead of the original covariance matrix to avoid filter divergence caused by non-positive definiteness of the covariance matrix. Furthermore, the SRUKF-SLAM algorithm has higher filtering efficiency than the UKF-SLAM algorithm, which is beneficial for real-time estimation of the robot pose. The above-mentioned Kalman filter and its derivative algorithms are only applicable to robot systems under the assumption of Gaussian noise. In practical application scenarios, the noise in the sensor information of mobile robot SLAM has a certain correlation, and the noise model often follows the non-Gaussian distribution [25; 26; 27].