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Hongjun Zhou

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3 papers

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AAAI Conference 2023 Conference Paper

3D Assembly Completion

Weihao Wang
Rufeng Zhang
Mingyu You
Hongjun Zhou
Bin He

Automatic assembly is a promising research topic in 3D computer vision and robotics. Existing works focus on generating assembly (e.g., IKEA furniture) from scratch with a set of parts, namely 3D part assembly. In practice, there are higher demands for the robot to take over and finish an incomplete assembly (e.g., a half-assembled IKEA furniture) with an off-the-shelf toolkit, especially in human-robot and multi-agent collaborations. Compared to 3D part assembly, it is more complicated in nature and remains unexplored yet. The robot must understand the incomplete structure, infer what parts are missing, single out the correct parts from the toolkit and finally, assemble them with appropriate poses to finish the incomplete assembly. Geometrically similar parts in the toolkit can interfere, and this problem will be exacerbated with more missing parts. To tackle this issue, we propose a novel task called 3D assembly completion. Given an incomplete assembly, it aims to find its missing parts from a toolkit and predict the 6-DoF poses to make the assembly complete. To this end, we propose FiT, a framework for Finishing the incomplete 3D assembly with Transformer. We employ the encoder to model the incomplete assembly into memories. Candidate parts interact with memories in a memory-query paradigm for final candidate classification and pose prediction. Bipartite part matching and symmetric transformation consistency are embedded to refine the completion. For reasonable evaluation and further reference, we design two standard toolkits of different difficulty, containing different compositions of candidate parts. We conduct extensive comparisons with several baseline methods and ablation studies, demonstrating the effectiveness of the proposed method.

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IROS Conference 2005 Conference Paper

Sensor planning for mobile robot localization - a hierarchical approach using Bayesian network and particle filter

Hongjun Zhou
Shigeyuki Sakane

In this paper we propose a hierarchical approach to solving sensor planning for the global localization of a mobile robot. Our system consists of two subsystems: a lower and a higher layer. The lower layer uses a particle filter to evaluate the posterior probability of the localization. When the particles converge into clusters, the higher layer starts particle clustering and sensor planning to generate an optimal sensing action sequence for the localization. The higher layer uses a Bayesian network for the probabilistic inference. The sensor planning takes into account both localization belief and sensing cost. We conducted simulations and actual robot experiments to validate our proposed approach.

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IROS Conference 2002 Conference Paper

Sensor planning for mobile robot localization using Bayesian network representation and inference

Hongjun Zhou
Shigeyuki Sakane

We propose a novel method to solve a kidnapped robot problem. A mobile robot plans its sensor actions to localize itself using Bayesian network inference. The system differs from traditional methods such as the simple Bayesian decision or top-down action selection based on a decision tree. In contrast, we represent the contextual relation between the local sensing results and beliefs about the global localization using Bayesian networks. Inference of the Bayesian network allows us to classify ambiguous positions of the mobile robot when the local sensing evidences are obtained. By taking into account the trade-off between the global localization belief degree and local sensing cost, we define an integrated utility function to decide the local sensing range, and obtain an optimal sensing plan and optimal Bayesian network structure based on this function. We conducted simulation and real robot experiments to validate our planning concept.

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