Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

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

Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments

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

Requirements and Limitations of Thermal Drones for Effective Search and Rescue in Marine and Coastal Areas

Search and rescue (SAR) is a vital line of defense against unnecessary loss of life. However, in a potentially hazardous environment, it is important to balance the risks associated with SAR action. Drones have the potential to help with the efficiency, success rate and safety of SAR operations as they can cover large or hard to access areas quickly. The addition of thermal cameras to the drones provides the potential for automated and reliable detection of people in need of rescue. We performed a pilot study with a thermal-equipped drone for SAR applications in Morecambe Bay. In a variety of realistic SAR scenarios, we found that we could detect humans who would be in need of rescue, both by the naked eye and by a simple automated method. We explore the current advantages and limitations of thermal drone systems, and outline the future path to a useful system for deployment in real-life SAR