A Mobile Robot Position Adjustment as a Fusion of Vision System and Wheels Odometry in Autonomous Track Driving

Abstract

Autonomous mobile vehicles need advanced systems to determine their exact position in a certain coordinate system. For this purpose, the GPS and the vision system are the most often used. These systems have some disadvantages, for example, the GPS signal is unavailable in rooms and may be inaccurate, while the vision system is strongly dependent on the intensity of the recorded light. This paper assumes that the primary system for determining the position of the vehicle is wheel odometry joined with an IMU (Internal Measurement Unit) sensor, which task is to calculate all changes in the robot orientations, such as yaw rate. However, using only the results coming from the wheels system provides additive measurement error, which is most often the result of the wheels slippage and the IMU sensor drift. In the presented work, this error is reduced by using a vision system that constantly measures vehicle distances to markers located in its space. Additionally, the paper describes the fusion of signals from the vision system and the wheels odometry. Studies related to the positioning accuracy of the vehicle with both the vision system turned on and off are presented. The laboratory averaged positioning accuracy result was reduced from 0.32 m to 0.13 m, with ensuring that the vehicle wheels did not experience slippage. The paper also describes the performance of the system during a real track driven, where the assumption was not to use the GPS geolocation system. In this case, the vision system assisted in the vehicle positioning and an accuracy of 0.2 m was achieved at the control points.

Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories

“…Table 4 presents the results obtained: Trajectories depicts which trajectories have been used in the iterative search; Strategy shows the acronym of the calibration strategy applied; Method describes the iterative function used (GA or fmin) and the number of trajectory repetitions used in the iterative search: (1) one or (5) five repetitions; M −1 shows the value of the kinematic matrix obtained as a result of the iterative search; CF CALIBRATION is the value of the average cost function obtained during the iterative search or training; CF VALIDATION is the average value of the cost function obtained with five additional calibration trajectories (the complete flower-shape); Improvement depicts the relative improvement of CF VALIDATION relative to the uncalibrated case (None strategy). The calibration strategies shown in Table 4 have been generally labeled as STX-GAZ and STX-FminZ, where X describes the group of trajectories considered (from A to E); GA refers to the use of the ga.m iterative function and Fmin to the fmincon.m iterative function; and Z is the number of repetitions of each calibration trajectory considered, a value that can be 1 or 5.…”

“…Gargiulo et al [4] estimated the mobile robot position and orientation by fusing information gathered from the wheels and an Inertial Measurement Unit (IMU). Zwierzchowski et al [5] used a similar approach and included the information gathered from a vision system that measures the distance between the robot and custom markers located in the surrounding space. Xue et al [6] fuses the information gathered from the wheels, an IMU and a 2D LIDAR in order to operate in diverse outdoor environments without any prior information.…”