SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
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DOI：
10.1109/icra.2018.8460558
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发表时间：
2018-05
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影响因子：
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通讯作者：
Bruno Bodin;Harry Wagstaff;Sajad Saeedi;Luigi Nardi;Emanuele Vespa;John Mawer;A. Nisbet;M. Luján;S. Furber;A. Davison;P. Kelly;M. O’Boyle
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作者：
Bruno Bodin;Harry Wagstaff;Sajad Saeedi;Luigi Nardi;Emanuele Vespa;John Mawer;A. Nisbet;M. Luján;S. Furber;A. Davison;P. Kelly;M. O’Boyle
同时定位与地图构建（SLAM）正在成为机器人技术和增强现实（AR）系统的关键组成部分。虽然已经提出了大量的SLAM算法，但很少有人致力于统一这些算法的接口，或者对它们的性能进行全面比较。这是一个问题，因为不同的SLAM应用可能有不同的功能性和非功能性需求。例如，基于手机的AR应用能量预算紧张，而无人机导航系统通常需要高精度。SLAMBench2是一个基准测试框架，用于在可扩展的数据集列表上评估现有的和未来的开源及闭源SLAM系统，同时使用可比较且明确规定的性能指标列表。它支持多种现有的SLAM算法和数据集，例如ElasticFusion、InfiniTAM、ORB - SLAM2、OKVIS，并且框架明确规定了集成新算法和数据集的简单方法。SLAMBench2是一个公开可用的软件框架，它为定量、可比较且可验证的实验研究提供了一个起点，以研究SLAM系统之间的权衡。
SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems.