Abstract
Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory.
Highlights
Localisation is a fundamental aspect in the area of mobile robotics
Relative localisation methods are generally conducted within the body frame of the platform, through the integration of techniques such as vision based odometry systems without image georeferencing [1,2], dead reckoning via inertial measurement units (IMU) [3,4] and wheel odometry to determine the speed of the robotic platform [5,6]
The errors for each anchor follow a normal distribution shown by the Gaussian plots in Figure 5, where the standard deviation is seen to be similar for each anchor
Summary
In order to achieve autonomy, a mobile unmanned robot must be equipped with a localisation or positioning system that consistently and precisely determines robot pose, i.e., position and heading, as it navigates throughout an environment. Localisation methods and techniques are classified into two categories, relative and absolute. Relative localisation methods are generally conducted within the body frame of the platform, through the integration of techniques such as vision based odometry systems without image georeferencing [1,2], dead reckoning via inertial measurement units (IMU) [3,4] and wheel odometry (in the case of ground based systems) to determine the speed of the robotic platform [5,6]. Absolute techniques refer to the localisation of co-ordinate reference frames that are external to the robot, for example the use of independent landmarks [7,8], known correspondence reference points [9] or a Global Navigation Satellite System (GNSS) [10]
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