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A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM
arXiv - CS - Robotics Pub Date : 2021-08-03 , DOI: arxiv-2108.01225
Jiahui Fu, Qiangqiang Huang, Kevin Doherty, Yue Wang, John J. Leonard

In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation tasks. However, measurement ambiguity then arises as objects may possess complete or partial object shape symmetries (e.g., due to occlusion), making it difficult or impossible to generate a single consistent object pose estimate. One idea is to generate multiple pose candidates to counteract measurement ambiguity. In this paper, we develop a novel approach that enables an object-based SLAM system to reason about multiple pose hypotheses for an object, and synthesize this locally ambiguous information into a globally consistent robot and landmark pose estimation formulation. In particular, we (1) present a learned pose estimation network that provides multiple hypotheses about the 6D pose of an object; (2) by treating the output of our network as components of a mixture model, we incorporate pose predictions into a SLAM system, which, over successive observations, recovers a globally consistent set of robot and object (landmark) pose estimates. We evaluate our approach on the popular YCB-Video Dataset and a simulated video featuring YCB objects. Experiments demonstrate that our approach is effective in improving the robustness of object-based SLAM in the face of object pose ambiguity.

中文翻译:

在基于对象的 SLAM 中提出歧义的多假设方法

在基于对象的同时定位和映射 (SLAM) 中,6D 对象姿势提供了对下游规划和操作任务有用的地标几何的紧凑表示。然而,由于对象可能具有完整或部分的对象形状对称性(例如,由于遮挡),因此会出现测量模糊性,从而难以或不可能生成单个一致的对象姿态估计。一种想法是生成多个候选姿势来抵消测量的模糊性。在本文中,我们开发了一种新方法,使基于对象的 SLAM 系统能够推理对象的多个姿态假设,并将这种局部模糊信息合成为全局一致的机器人和地标姿态估计公式。特别是,我们(1)提出了一个学习姿态估计网络,它提供了关于一个物体的 6D 姿态的多个假设;(2) 通过将我们网络的输出视为混合模型的组成部分,我们将姿势预测合并到 SLAM 系统中,该系统通过连续观察恢复机器人和物体(地标)姿势估计的全局一致集。我们在流行的 YCB 视频数据集和具有 YCB 对象的模拟视频上评估我们的方法。实验表明,我们的方法可以有效地提高基于对象的 SLAM 在面对对象姿态模糊时的鲁棒性。恢复一组全局一致的机器人和物体(地标)姿态估计。我们在流行的 YCB 视频数据集和具有 YCB 对象的模拟视频上评估我们的方法。实验表明,我们的方法可以有效地提高基于对象的 SLAM 在面对对象姿态模糊时的鲁棒性。恢复一组全局一致的机器人和物体(地标)姿态估计。我们在流行的 YCB 视频数据集和具有 YCB 对象的模拟视频上评估我们的方法。实验表明,我们的方法可以有效地提高基于对象的 SLAM 在面对对象姿态模糊时的鲁棒性。
更新日期:2021-08-04
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