Simultaneous Localization and Mapping Related Datasets: A Comprehensive Survey
This survey addresses the problem of selecting appropriate datasets for SLAM researchers and practitioners, aiming to improve the rigor of evaluation and guide future dataset creation.
This paper surveys existing datasets for Simultaneous Localization and Mapping (SLAM), identifying issues like overexposure and biased evaluation. It aims to guide dataset selection and promote future dataset research by covering collection methodologies, task taxonomies, and evaluation criteria.
Due to the complicated procedure and costly hardware, Simultaneous Localization and Mapping (SLAM) has been heavily dependent on public datasets for drill and evaluation, leading to many impressive demos and good benchmark scores. However, with a huge contrast, SLAM is still struggling on the way towards mature deployment, which sounds a warning: some of the datasets are overexposed, causing biased usage and evaluation. This raises the problem on how to comprehensively access the existing datasets and correctly select them. Moreover, limitations do exist in current datasets, then how to build new ones and which directions to go? Nevertheless, a comprehensive survey which can tackle the above issues does not exist yet, while urgently demanded by the community. To fill the gap, this paper strives to cover a range of cohesive topics about SLAM related datasets, including general collection methodology and fundamental characteristic dimensions, SLAM related tasks taxonomy and datasets categorization, introduction of state-of-the-arts, overview and comparison of existing datasets, review of evaluation criteria, and analyses and discussions about current limitations and future directions, looking forward to not only guiding the dataset selection, but also promoting the dataset research.