SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding
SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding
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DOI：
10.1109/icra.2019.8794369
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发表时间：
2019-05
期刊：
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通讯作者：
Mihai Bujanca;Paul Gafton;Sajad Saeedi;A. Nisbet;Bruno Bodin;M. O’Boyle;A. Davison;P. Kelly;G. Riley;B. Lennox;M. Luján;S. Furber
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作者：
Mihai Bujanca;Paul Gafton;Sajad Saeedi;A. Nisbet;Bruno Bodin;M. O’Boyle;A. Davison;P. Kelly;G. Riley;B. Lennox;M. Luján;S. Furber
随着SLAM研究领域的成熟和可用SLAM系统数量的增加，对可以根据先前工作客观评估它们的框架的需求也在增长。这个新版本的SLAMBench超越了传统的视觉SLAM，并为场景理解和非刚性环境（动态SLAM）提供了新的支持。更具体地说，对于动态SLAM，SLAMBench 3.0包括DynamicFusion的第一个公开实现，沿着评估基础设施。此外，我们还包括两个SLAM系统（一个密集，一个稀疏），它们使用卷积神经网络进行场景理解，以及数据集和适当的度量。通过一系列用例，我们展示了新纳入的算法，可视化辅助工具和指标（6个新指标，4个新数据集和5个新算法）。
As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 new datasets and 5 new algorithms).