Ziqi Lu

I am a PhD student from the Marine Robotics Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT. My thesis advisor is Prof. John Leonard.

My research interests lie in the areas of robot autonomy, machine learning, and computer vision. I am particularly interested in developing robust object-centric robot perception and mapping systems.

I received my M.S.('19) degree from the University of California, Berkeley and B.S.('17) degree from Shanghai Jiao Tong University.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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News

[Oct 2023]  I'll be joining the Autonomous Vehicle Research Group of NVIDIA as a research intern in Spring 2024!

[Apr 2023]  I'll be joining the Octant team of Amazon AWS AI as a research scientist intern this summer!

[Dec 2022]  Check out our Awesome-Object-SLAM repo for a list of object SLAM papers and resources.

[Jul 2022]   Our work on SLAM-Supported Self-Training for 6D Object Pose Estimation has been accepted to IROS 2022.

[Apr 2022]  Our work on Discrete-Continuous Smoothing and Mapping has been selected for the spotlight session (top 2 of 48) in the ICRA 2022 workshop on Robotic Perception and Mapping: Emerging Techniques.

[Jan 2022]  I'll be joining the Surreal team in the Meta Reality Labs as a research scientist intern this summer!

[Jul 2021]   Our work on Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM has been accepted to IROS 2021.

Selected Publications
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Fast Sparse View Guided NeRF Update for Object Reconfigurations


Ziqi Lu, Jianbo Ye, Xiaohan Fei, Xiaolong Li, Jiawei Mo, Ashwin Swaminathan, Stefano Soatto
ArXiv, 2024
arxiv / website /

Fast sparse-view guided NeRF update to physical object reconfigurations.
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SLAM-Supported Self-Training for 6D Object Pose Estimation


Ziqi Lu, Yihao Zhang, Kevin Doherty, Odin Severinsen, Ethan Yang, John Leonard
IROS, 2022
arxiv / code /

A robust-pose-graph-optimization-aided self-training method for domain adaptation of 6D object pose estimators.
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Discrete-Continuous Smoothing and Mapping


Kevin Doherty, Ziqi Lu, Kurran Singh, John Leonard
arXiv preprint, 2022
arxiv / video / code /

Selected for the spotlight session (top 2 of 48) in the ICRA 2022 workshop on Robotic Perception and Mapping: Emerging Techniques.

A novel solver to efficiently recover approximate solutions for discrete-continuous (hybrid) factor graph optimization problems in robotic applications.
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Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM


Ziqi Lu*, QiangQiang Huang*, Kevin Doherty, John Leonard
IROS, 2021
paper / arxiv / video /

A real-time object-based SLAM system that is robust to symmetry- or occlusion-induced pose ambiguity from individual 6D object pose predictions.




Design and source code from Leonid Keselman's Jekyll fork of Jon Barron's website