Enan's Personal Website

Sadman Sakib Enan

Advanced R&D Scientist, Honeywell Aerospace | PhD in CS, University of Minnesota

Area of interest: Robot Vision & Perception, Deep Learning, Inertial Navigation, Filtering & Estimation, GPS-denied Solutions
Summary: I specialize in developing end-to-end vision-based perception frameworks for autonomous robots using machine learning and AI algorithms. I have extensive experience implementing and validating deep learning frameworks on real robots and embedded GPUs. I am well-versed in vision-based navigation, pose estimation, activity recognition, and image segmentation techniques. Additionally, I have significant experience working with Kalman filters and inertial navigation systems. Currently, I am focusing on alternative navigation techniques for aircraft in GPS-denied environments.

NEWS

(Mar'25) Two abstracts are accepted to be presented at ION Joint Navigation Conference (JNC 2025).
(Sep'24) Attended ION GNSS+ Conference in Baltimore, Maryland.
(May'24) Started working at Honeywell Aerospace as an Advanced R&D Scientist in the Controls, Navigation, and Surveillance group.
(May'24) Awarded the degree of Doctor of Philosophy from the University of Minnesota.
(Apr'24) Defended my doctoral dissertation titled "Vision-Based Computational Methods Towards Effective Underwater Multi-Human-Robot Interaction".
(Jan'24) Paper got accepted for publication in IEEE Robotics and Automation Letters (RA-L).
(Aug'23) Passed Doctoral Dissertation Proposal Exam.
(Jun'22) Paper got accepted for presentation at IROS 2022, Kyoto, Japan. (nomination: selected as Best Paper Award Finalist on Cognitive Robotics)
Archived news.

PAST Projects

Diver Identification Using Anthropometric Data Ratios (2024 RA-L)

Recent advances in efficient design, perception algorithms, and computing hardware have made it possible to create improved human-robot interaction (HRI) capabilities for autonomous underwater vehicles (AUVs). To conduct secure missions as underwater human-robot teams, AUVs require the ability to accurately identify divers. However, this remains an open problem due to divers' challenging visual features, mainly caused by similar-looking scuba gear. In this paper, we present a novel algorithm that can perform diver identification using either pre-trained models or models trained during deployment. We exploit anthropometric data obtained from diver pose estimates to generate robust features that are invariant to changes in distance and photometric conditions. We also propose an embedding network that maximizes inter-class distances in the feature space and minimizes those for the intra-class features, which significantly improves classification performance. Furthermore, we present an end-to-end diver identification framework that operates on an AUV and evaluate the accuracy of the proposed algorithm. Quantitative results in controlled-water experiments show that our algorithm achieves a high level of accuracy in diver identification.
Project page: https://irvlab.cs.umn.edu/projects/human-robot-collaboration/diver-identification-using-anthropometric-data-ratios

Robotic Detection of a Human-Comprehensible Gestural Language (2022 IROS BEST PAPER FINALIST)

We present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easily understood by divers observing the conversation - unlike typical radio frequency, light, or audio-based AUV communication. To allow AUVs to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting maximally discriminative spatio-temporal features. We train this network on diverse simulated and real-world data. Our experimental evaluations, both in simulation and in closed-water robot trials, demonstrate that the proposed RRCommNet architecture is able to decipher gesture-based messages with an average accuracy of 88-94% on simulated data and 73-83% on real data.
Project page: https://irvlab.cs.umn.edu/projects/human-robot-collaboration/robotic-detection-human-comprehensible-gestural-language

Underwater Image Super-Resolution using Deep Residual Multipliers (2020 ICRA)

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of ‘high’ (640 x 480) and ‘low’ (80 x 60, 160 x 120, and 320 x 240) resolution. USR-248 contains paired instances for supervised training of 2x, 4x, or 8x SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models’ performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions.
Project page: http://irvlab.cs.umn.edu/image-enhancement-and-super-resolution/srdrm-and-srdrm-gan

Design and Experiments with LoCO AUV: A Low Cost Open-Source Autonomous Underwater Vehicle (2020 IROS)

We present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform’s preliminary local motion control and state estimation abilities, which enable it to perform maneuvers autonomously. In order to demonstrate its usefulness for a variety of tasks, we implement a variety of our previously presented human-robot interaction capabilities on LoCO, including gestural control, diver following, and robot communication via motion. Finally, we discuss the practical concerns of deployment and our experiences in using this robot in pools, lakes, and the ocean. All design details, instructions on assembly, and code will be released under a permissive, open-source license.
Project page: http://irvlab.cs.umn.edu/other-projects/loco-auv

Selected Publications

SS Enan and J Sattar, "Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaboration," (Under review).
SS Enan and J Sattar, "Visual Detection of Diver Attentiveness for Underwater Human-Robot Interaction," (Under review).
J Hong*, SS Enan* (*equal contribution), and J Sattar, "Diver Identification Using Anthropometric Data Ratios for Underwater Multi-Human-Robot Collaboration," IEEE Robotics and Automation Letters (RA-L), 2024.
SS Enan, M Fulton, and J Sattar, "Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 3085-3092.
C Edge, SS Enan, M Fulton, J Hong, J Mo, K Barthelemy, H Bashaw, B Kallevig, C Knutson, K Orpen, and J Sattar, "Design and Experiments with LoCO AUV: A Low Cost Open-Source Autonomous Underwater Vehicle," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, 2020, pp. 1761-1768.
MJ Islam, SS Enan, P Luo, and J Sattar, "Underwater Image Super-Resolution using Deep Residual Multipliers," in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 900-906.

Community

Reviewer

IEEE Robotics and Automation Letters (RA-L): 2021.
IEEE Journal of Oceanic Engineering (JOE): 2022.
IEEE International Conference on Robotics and Automation (ICRA): 2021, 2023, 2024.
International Conference on Intelligent Robots and Systems (IROS): 2020, 2022.

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