Mohamad Qadri

I am a PhD student at the Robotics Institute at Carnegie Mellon University, where I work with Michael Kaess in the the Robotics Perception Lab . I work on statistical methods for state estimation. I am interested in developing algorithms for efficient and robust inference at the intersection of linear algebra and probabilistic graphical models.

I completed my Masters in Robotics at CMU working with George Kantor on Simultaneous Localization and Mapping (SLAM) in agricultural environments. Prior to that, I worked with Simon Lucey and Laszlo Jeni on monocular 3D reconstruction. In my undergrad, I majored in Electrical Engineering at the University of Maryland - College Park.

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Updates

[Apr '24]  

Our paper "AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion " was accepted to SIGGRAPH 2024.

[Jan '24]  

Our paper "Learning Covariances for Estimation with Constrained Bilevel Optimization" was accepted to ICRA 2024.

[Jun '23]  

Our paper "Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation" was accepted to the Differentiable Almost Everything Workshop at ICML 2023.

[Jun '23]  

Presented our work on neural surface reconstruction using imaging sonar at the Computational Cameras and Displays (CCD) Workshop at CVPR 2023

[Jan '23]  

Our paper Neural Implicit Surface Reconstruction using Imaging Sonar was accepted to ICRA 2023.

[Jan '23]  

Our paper Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping was accepted to ICRA 2023.

[Jan '23]  

Our paper Runahead A*: Speculative Parallelism for A* with Slow Expansions was accepted to ICAPS 2023.
Research

AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion
Mohamad Qadri*, Kevin Zhang*, Akshay Hinduja, Michael Kaess, Adithya Pediredla, Christopher A. Metzler
SIGGRAPH, 2024 (Conference Proceedings)
arXiv

We present a technique to fuse acoustic and optical measurements for 3D reconstruction. Our framework can reconstruct accurate high-resolution 3D surfaces from measurements captured over heavily-restricted baselines.

Learning Covariances for Estimation with Constrained Bilevel Optimization
Mohamad Qadri, Zachary Manchester, Michael Kaess
ICRA, 2024
arXiv / video

We propose a gradient-based method to estimate well-conditioned covariance matrices for estimation. We formulate the the learning procedure as a constrained bilevel optimization problem over factor graphs.

Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation
Mohamad Qadri, Michael Kaess
ICML, 2023 (Differentiable Almost Everything Workshop)
arXiv

We propose a gradient-based method for learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Our algorithm converges much quicker to model estimates that lead to solutions of higher quality compared to existing methods

Neural Implicit Surface Reconstruction using Imaging Sonar
Mohamad Qadri, Michael Kaess, Ioannis Gkioulekas
ICRA, 2023
arXiv / video / code

We present a technique for dense 3D reconstruction of objects using an imaging sonar. We represent the geometry as a neural implicit function.

Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping
Tianxiang Lin, Akshay Hinduja, Mohamad Qadri, Michael Kaess
ICRA, 2023
arXiv / video

This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images.

Runahead A*: Speculative Parallelism for A* with Slow Expansions
Mohammad Bakhshalipour, Mohamad Qadri, Seyed Borna Ehsani, Dominic Guri, Maxim Likhachev Phillip B. Gibbons
ICAPS, 2023

We go beyond traditional parallelism of the A* algorithm and introduce RA* (Runahead A*) which performs speculative parallelism of A*.

InCOpt: Incremental Constrained Optimization Using the Bayes Tree
Mohamad Qadri, Paloma Sodhi, Joshua G. Mangelson, Frank Dellaert, Michael Kaess
IROS, 2022
paper / video / code

We present an Augmented Lagrangian-based incremental constrained optimizer that views matrix operations as message passing over the Bayes tree.

RACOD: algorithm/hardware co-design for mobile robot path planning
Mohammad Bakhshalipour, Seyed Borna Ehsani, Mohamad Qadri, Dominic Guri, Maxim Likhachev Phillip B. Gibbons
ISCA, 2022
paper

RACOD is an algorithm/hardware co-design for mobile robot path planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm extension for runahead path exploration.

Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision
Harry Freeman, Mohamad Qadri, Abhisesh Silwal, Paul O'Connor, Zachary Rubinstein, Daniel Cooley, George Kantor
In Submission at IEEE Transactions on Robotics (T-RO)
arXiv / video

We develop a computer vision method to size and track the growth rates of apple fruitlets. Fruitlets are sized using a combination of deep learning algorithms and are associated across days using a novel Attentional Graph Neural Network approach.

Toward Semantic Scene Understanding for Fine-Grained 3D Modeling of Plants
Mohamad Qadri, Harry Freeman, , Eric Schneider , George Kantor
AIAFS AAAI, (2021)
paper / video

In this paper, we use semantics and environmental priors to construct accurate 3D maps of agricultural environments.

Robotic Vision for 3D Modeling and Sizing in Agriculture
Mohamad Qadri,
Masters thesis
paper

Other Projects

A Study of the Theoretical Foundations of Variational and Score Matching-based Diffusion Models
Ye Won Byun, Mohamad Qadri
pdf / code
Semi-Supervised Learning via Offline Pseudolabel Generation and Consistency Regularization
Mohamad Qadri, Maggie Collier
pdf / code
Exploring the link between Geodesically Convex Optimization and Contraction Analysis
Mohamad Qadri, Chiheb Boussemma
pdf
A study of Joint-Space Control of Non-Linear Robotics manipulators
Mohamad Qadri
pdf

Source code