Perception systems
Perception systems
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The NODE Lab’s research in perception systems focuses on developing computationally efficient, multi-modal systems (including visual, LiDAR, and radar) inspired by human visual perception. Leveraging physics-informed and explainable AI, these systems aim to semantically detect and delineate regions of interest, reliably extract features, and create accurate covisibility graphs for precise pose estimation in harsh and unstructured environments. To enhance the reliability of perception and decision-making, communication between autonomous agents is utilized for remote state estimation and distributed control. This includes developing i) uncertainty-aware LiDAR-based SLAM systems and ii) stereo visual odometry pipelines for networked robots and connected automated driving systems.
Related publications
Related publications
Marcelo Contreras, Neel P Bhatt, Ehsan Hashemi
DynaNav-SVO: Dynamic Stereo Visual Odometry With Semantic-Aware Perception for Autonomous Navigation
IEEE Transactions on Intelligent Vehicles
Hadis Saadati Nezhad, Marcelo Contreras, André McDonald, Ehsan Hashemi
Visual Odometry for Mobile Material Deposition Systems Using RGB-D Cameras
International Thermal Spray Conference 2024
[Link]
Marcelo Contreras, Aayush Jain, Neel Pratik Bhatt, Arunava Banerjee, Ehsan Hashemi
A survey on 3D Object Detection in Real Time for Autonomous Driving
Frontiers in Robotics and AI
[Link]
Neel P Bhatt, Ruihe Zhang, Minghao Ning, Ahmad Reza Alghooneh, Joseph Sun, Pouya Panahandeh, Ehsan Mohammadbagher, Ted Ecclestone, Ben MacCallum, Ehsan Hashemi, Amir Khajepour
WATonoBus: An All Weather Autonomous Shuttle
ArXiv
[Link]
Marcelo Contreras, Neel P Bhatt, Ehsan Hashemi
A Stereo Visual Odometry Framework with Augmented Perception for Dynamic Urban Environments
IEEE ITSC 2023
[Link]
S Maleki, A McDonald, E Hashemi
Visual Navigation for Autonomous Mobile Material Deposition Systems using Remote Sensing
ITSC 2023
[Link]
Niraj Reginald, Omar Al-Buraiki, Baris Fidan, Ehsan Hashemi
Confidence Estimator Design for Dynamic Feature Point Removal in Robot Visual-Inertial Odometry
IEEE IECON 2022
[Link]
Ali Salimzadeh, Neel P Bhatt, Ehsan Hashemi
Augmented visual localization using a monocular camera for autonomous mobile robots
IEEE CASE 2022
[Link]
Daniel Flögel, Neel Pratik Bhatt, Ehsan Hashemi
Infrastructure-aided localization and state estimation for autonomous mobile robots
MDPI Robotics
[Link]
Teng Li, Hongjun Xing, Ehsan Hashemi, Hamid D. Taghirad, Mahdi Tavakoli
A brief survey of observers for disturbance estimation and compensation
Robotica
[Link]
Ehsan Hashemi, Amir Khajepour, Nikolai Moshchuk, Shih-Ken Chen
Real-Time Road Bank Estimation With Disturbance Observers for Vehicle Control Systems
IEEE Transactions on Control Systems Technology
[Link]
Ehsan Mohammadbagher, Neel P Bhatt, Ehsan Hashemi, Baris Fidan, Amir Khajepour
Real-time Pedestrian Localization and State Estimation Using Moving Horizon Estimation
IEEE ITSC 2020
[Link]
Yiwen Liao, Ehsan Hashemi, Tong Wang, Bin Yang
A Learning-Aided Generic Framework for Fault Detection and Recovery of Inertial Sensors in Automated Driving Systems
IEEE Systems Journal
[Link]
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