Evaluating indoor positioning errors

With the advent of the indoor positioning field there has been a plethora of different approaches to locating people and assets in indoor environments, each with their own strengths and weaknesses. Despite this, a lot of the metrics used for optimization may contain hidden biases (unintended or neglected) or end up measuring aspects of the solution which conflict with the intended use case. In this position paper we hope to initiate a discussion on standardized baselines and metrics, and suggest some normalizing measures to alleviate bias and further a standardization of error metrics.