Abstract
In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version.
Highlights
Positional assurance is an imperative and heavily researched area within unmanned robotic systems and technology [1]
As the intended deployment environment of the Unmanned Ground Vehicle (UGV) was an indoor space, a corridor with a similar wall, ceiling and floor construction was chosen as the testing environment
This paper demonstrates a method for counteracting increased variance in sensors due to variable factors encountered during operation and presents an alternate method for representing the sensor measurement variance in a state estimation system
Summary
Positional assurance is an imperative and heavily researched area within unmanned robotic systems and technology [1]. The ability to precisely estimate a robot position, or for the robot to be capable of localising itself within the environment, is a vital topic to consider prior to the integration of such autonomous systems into modern day operations [2]. The ability to assess the effectiveness of such a system is a direct requirement for Beyond Visual Line Of Sight (BVLOS) operation [3]. BVLOS is usually considered at ranges greater at 500 m. BVLOS operation may be considered within short range, enclosed cluttered environments involving buildings or obstructions and/or where transitions between indoor and outdoor environments may occur [4]. A traditional and well-documented technique commonly deployed for this purpose is Global Navigation Satellite
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