 Simultaneous Localization and Mapping (SLAM) has found an increasing utilization lately, such as self-driving cars, robot navigation, 3D mapping, virtual reality (VR) and augmented reality (AR), etc., empowering both industry and daily life. Although the state-of-the-art algorithms where developers have spared no effort are source of intelligence, it is the datasets that dedicate behind and raise us higher. The employment of datasetsis essentially a kind of simulation but profits many aspects - capacity of drilling algorithm hourly, exemption of costly hardware and ground truth system, and equitable benchmark for evaluation. However, as a branch of great significance, still the datasets have not drawn wide attention nor been reviewed thoroughly. Hence in this article, we strive to give a comprehensive and open access review of SLAM related datasets and evaluation, which are scarcely surveyed while highly demanded by researchers and engineers, looking forward to serving as not only a dictionary but also a development proposal. The paper starts with the methodology of dataset collection, and a taxonomy of SLAM related tasks. Then followed with the main portion - comprehensively survey the existing SLAM related datasetsby category with our considerate introductions and insights. Furthermore, we talk about the evaluation criteria, which are necessary to quantify the algorithm performance on the dataset and inspect the defects. At the end, we summarize the weakness of datasets and evaluation - which could well result in the weakness of topical algorithms - to promote bridging the gap fundamentally .
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 .
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Due to the complicated procedure and costly hardware,
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found an increasing utilization lately, such as self-driving cars, robot navigation, 3D mapping, virtual reality (VR) and augmented reality (AR), etc., empowering both industry and daily life. Although the state-of-the-art algorithms where developers have spared no effort are source of intelligence, it is the datasets that dedicate behind and raise us higher. The employment of datasetsis essentially a kind of simulation but profits many aspects - capacity of drilling algorithm hourly, exemption of costly hardware and ground truth system, and equitable benchmark for evaluation. However, as a branch of great significance, still the datasets have not drawn wide attention nor been reviewed thoroughly. Hence in this article, we strive to give a comprehensive and open access review of
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
R
and evaluation, which are scarcely surveyed while highly demanded by researchers and engineers, looking forward to serving as not only a dictionary but also a development proposal. The paper starts with the methodology of dataset collection, and a taxonomy of SLAM related tasks. Then followed with the main portion - comprehensively survey the existing SLAM related datasetsby category with our considerate introductions and insights. Furthermore, we talk about the
, 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
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which are necessary to quantify the algorithm performance on the dataset and inspect the defects. At the end, we summarize the weakness of datasets and evaluation - which could well result in the weakness of topical algorithms - to promote bridging the gap fundamentally
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