Robust Exploration with Multiple Hypothesis Data Association
Robust Exploration with Multiple Hypothesis Data Association
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DOI：
10.1109/iros.2018.8593753
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发表时间：
2018-10
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影响因子：
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通讯作者：
Jinkun Wang;Brendan Englot
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文献类型：
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作者：
Jinkun Wang;Brendan Englot
我们研究了同时定位和映射（SLAM）所面临的模糊数据关联问题，特别是针对缺乏丰富特征的环境的自主探索。在这样的环境中，单个误报赋值可能导致灾难性的失败，即使是健壮的后端也可能无法解决。在多假设跟踪的启发下，我们提出了一种有效管理数据关联多假设（MH）的新方法，该方法继承了传统的联合兼容分支定界（JCBB）方法，包括假设的生成、排序和消除。我们分析了MHJCBB在两种特定情况下的性能，一种是将其应用于预定义轨迹上的SLAM，另一种是在探索未知环境时显示其适用性。统计结果表明，MHJCBB在模糊条件下保持多种假设，显著提高了地图精度。
We study the ambiguous data association problem confronting simultaneous localization and mapping (SLAM), specifically for the autonomous exploration of environments lacking rich features. In such environments, a single false positive assignment might lead to catastrophic failure, which even robust back-ends may be unable to resolve. Inspired by multiple hypothesis tracking, we present a novel approach to effectively manage multiple hypotheses (MH) of data association inherited from traditional joint compatibility branch and bound (JCBB), which entails the generation, ordering and elimination of hypotheses. We analyze the performance of MHJCBB in two particular situations, one applying it to SLAM over a predefined trajectory and the other showing its applicability in exploring unknown environments. Statistical results demonstrate that MHJCBB's maintenance of diverse hypotheses under ambiguous conditions significantly improves map accuracy.