Characterizing SLAM Benchmarks and Methods for the Robust Perception Age
Characterizing SLAM Benchmarks and Methods for the Robust Perception Age
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发表时间：
2019-05
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通讯作者：
Wenkai Ye;Yipu Zhao;P. Vela
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作者：
Wenkai Ye;Yipu Zhao;P. Vela
SLAM基准测试的多样性为SLAM算法提供了广泛的测试，以便了解它们的性能，无论是单独的性能还是相对性能。在评估性能时，这些基准测试的临时创建并不一定能揭示SLAM算法的特定弱点。在本文中，我们建议使用决策树来识别对最先进的SLAM算法具有挑战性的基准属性，以及SLAM流程中关于其应对这些挑战能力的重要组件。如果我们要深入了解稳健的SLAM算法的核心计算需求，确定特定序列的哪些因素相对于这些特性会导致跟踪失败或性能下降是很重要的。同样，我们认为在进行基准测试时对单个SLAM组件的计算性能进行分析是很重要的。特别是，我们提倡在ROS数据包回放期间使用时间拉伸，或者我们称之为慢动作回放。使用慢动作对SLAM实例进行基准测试可以为如何在计算组件层面改进SLAM实现提供线索。我们在从基准特性生成的选定典型序列上对三种流行的视觉里程计/ SLAM算法以及我们自己的两种低延迟算法进行了测试，以进一步展示从计算高效组件中获得的优势。
The diversity of SLAM benchmarks affords extensive testing of SLAM algorithms to understand their performance, individually or in relative terms. The ad-hoc creation of these benchmarks does not necessarily illuminate the particular weak points of a SLAM algorithm when performance is evaluated. In this paper, we propose to use a decision tree to identify challenging benchmark properties for state-of-the-art SLAM algorithms and important components within the SLAM pipeline regarding their ability to handle these challenges. Establishing what factors of a particular sequence lead to track failure or degradation relative to these characteristics is important if we are to arrive at a strong understanding for the core computational needs of a robust SLAM algorithm. Likewise, we argue that it is important to profile the computational performance of the individual SLAM components for use when benchmarking. In particular, we advocate the use of time-dilation during ROS bag playback, or what we refer to as slo-mo playback. Using slo-mo to benchmark SLAM instantiations can provide clues to how SLAM implementations should be improved at the computational component level. Three prevalent VO/SLAM algorithms and two low-latency algorithms of our own are tested on selected typical sequences, which are generated from benchmark characterization, to further demonstrate the benefits achieved from computationally efficient components.