A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM

Jiahui Fu; John J. Leonard; Kevin Doherty; Qiangqiang Huang; Yue Wang

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A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM

Jiahui Fu, Qiangqiang Huang, Kevin Doherty, Yue Wang, John J. Leonard

Robotics · Submitted 3 Aug 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experiments demonstrate that our approach is effective in improving the robustness of object-based SLAM in the face of object pose ambiguity.

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arXiv ID2108.01225 (version 1)

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CitationJiahui Fu, John J. Leonard, Kevin Doherty, Qiangqiang Huang, Yue Wang. "A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM." arXiv:2108.01225 (2021). https://pith.science/paper/RB3WJJNL

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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.

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#	sentence	role	the authors' own work	states the main conclusion	strength
s0	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.	background 1.00	0.06	0.02	asserted 0.98
s1	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.	background 0.90	0.06	0.02	hedged possibility 0.77
s2	One idea is to generate multiple pose candidates to counteract measurement ambiguity.	background 0.84	0.07	0.03	hedged possibility 0.66
s3	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.	contribution 1.00	0.95	0.24	asserted 1.00
s4	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.	method 0.64	0.96	0.11	asserted 1.00
s5	We evaluate our approach on the popular YCB-Video Dataset and a simulated video featuring YCB objects.	method 0.95	0.94	0.03	asserted 0.65
s6	Experiments demonstrate that our approach is effective in improving the robustness of object-based SLAM in the face of object pose ambiguity.	result 0.79	0.95	0.86	asserted 1.00

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Original abstract

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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Uses the method or tool of 2 papers

To facilitate comparison within the YCB-Video Dataset benchmark, we follow two metrics used in [ref] and [ref] , i.e., ADD (introduced in [ref]) and ADD-S.

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes · Metrics · 0.84
To facilitate comparison within the YCB-Video Dataset benchmark, we follow two metrics used in [ref] and [ref] , i.e., ADD (introduced in [ref]) and ADD-S.

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking · Metrics · 0.84

Uses data or values from 1 paper

The YCB-Video Dataset [ref] is built on 21 YCB objects [ref] placed in various indoor scenes and consists of 92 video sequences with 133,827 images in all.

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes · Datasets · 0.95

Compares with 1 paper

Quantitatively, we compare our MHPE results to those of PoseCNN and the more recent PoseRBPF [ref] , all with RGB images (see Table [ref]) and object detection results from PoseCNN for the purpose of fair comparison.

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking · Results on YCB-Video Dataset · 1.00

Cites as background 2 papers

With the advancement in deep learning, PoseCNN [ref] and SSD-6D [ref] , as two pioneering methods for estimating object pose in cluttered scenes, use neural networks to perform object detection and pose estimation at the same time.

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes · 6D Object Pose Estimation · 0.97
DOPE [ref] uses only synthetic training data for robot manipulation tasks and the network shows good generalization to novel environments.

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects · 6D Object Pose Estimation · 0.92

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