###### Abstract
Generally, during the sensor manufacturing process, internal calibration is performed to obtain six-axis force and torque data, and the resulting calibration matrix is provided to users [4]. Some studies have proposed methods to collect the sensor's raw data and determine a calibration matrix using artificial neural networks [5; 6; 7; 8]. The calibration matrix obtained through this process enables users to retrieve force and torque data from the sensor. While this provides calibration at the sensor level, additional steps are required for users to obtain accurate data when an end tool is attached or the base is inclined. Research has been conducted to determine parameters such as bias, CoM, and gravity by sampling in various postures and modeling the sensor system [9; 10; 11; 12]. Methods have been proposed to extract physical information using sensor data from specified postures and the robot's kinematics, but these approaches have not considered base incline. More recently, studies have aimed to calibrate for an inclined base [13]. However, these methods have the drawback of requiring data collection with at least three end tools of known mass and CoM, making it unsuitable for applications like mobile manipulators, where the base's incline changes frequently. Also, current researchers utilize the raw data of the sensor to make their own sensor model [14; 15]. In the case of a mobile manipulator, if calibration is time-consuming, the preparation time can increase exponentially each time a new work location is added. Indeed, manufacturers of FT sensors typically calibrate the internal parameters of the sensor, enabling users to obtain six-axis force and torque measurements. However, our goal is not to recalibrate these internal parameters using raw data but rather to accurately decompose the forces and torques in real-world scenarios where the sensor is used, such as when an end tool is attached to the sensor or when the base is inclined. Current research typically focuses on determining the parameters of a manipulator fixed in a specific environment and compensating for the effects on the FT sensor using the calculated parameters. However, these approaches have limitations: when the robot's installation location changes, causing changes in leveling, excessive resistance, or degradation, the known joint position data may no longer be accurate, requiring the user to repeat the calibration process. As a result, these methods are not suitable for mobile manipulators or robots with small payloads that frequently encounter changing environments. To address these challenges, we propose a method for quickly estimating the parameters necessary for accurate external force estimation using FT sensor data collected from several specific postures. First, we propose a generalized sensor model consisting of parameters such as the CoM, crosstalk matrix, and inclination, ensuring that sensor compensation is complete once all parameters are identified. By using the force and torque data provided by the sensor, rather than relying on raw sensor data, our model enhances generality and applicability across various sensor types and manufacturers. Second, instead of directly utilizing the sensor's raw data, we perform calibration using only the force and torque data provided by the sensor. This ensures that our proposed method can be applied generally, regardless of the sensor type or manufacturer. Third, our method enables on-site calibration without requiring additional sensors or equipment, even when precise information about the operating environment of the manipulator-mounted FT sensor (such as the CoM of the end tool or the inclination of the base) is unknown. This is particularly relevant in scenarios where the FT sensor is used with an end tool, as users often lack accurate knowledge of the end tool's physical properties or the degree of ground inclination. Our approach allows these environmental factors to be determined through calibration and compensated for in the sensor data, enabling accurate force and torque measurements.