no code implementations • 27 Apr 2024 • Nikolaos Stathoulopoulos, Björn Lindqvist, Anton Koval, Ali-akbar Agha-mohammadi, George Nikolakopoulos
In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented.
no code implementations • 26 Mar 2024 • Jinrae Kim, Sunggoo Jung, Sung-Kyun Kim, Youdan Kim, Ali-akbar Agha-mohammadi
From the ROI, line segment features are extracted using a deep line segment detection algorithm.
no code implementations • 18 Apr 2023 • Mario A. V. Saucedo, Akash Patel, Rucha Sawlekar, Akshit Saradagi, Christoforos Kanellakis, Ali-akbar Agha-mohammadi, George Nikolakopoulos
In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame.
no code implementations • 22 Jan 2023 • Nikolaos Stathoulopoulos, Anton Koval, Ali-akbar Agha-mohammadi, George Nikolakopoulos
This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses.
no code implementations • 15 Oct 2022 • Jason M. Gregory, Sarah Al-Hussaini, Ali-akbar Agha-mohammadi, Satyandra K. Gupta
Experimental design in field robotics is an adaptive human-in-the-loop decision-making process in which an experimenter learns about system performance and limitations through interactions with a robot in the form of constructed experiments.
no code implementations • 12 Sep 2022 • Joshua Ott, Sung-Kyun Kim, Amanda Bouman, Oriana Peltzer, Mamoru Sobue, Harrison Delecki, Mykel J. Kochenderfer, Joel Burdick, Ali-akbar Agha-mohammadi
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors.
no code implementations • 2 Aug 2022 • Robin Schmid, Deegan Atha, Frederik Schöller, Sharmita Dey, Seyed Fakoorian, Kyohei Otsu, Barry Ridge, Marko Bjelonic, Lorenz Wellhausen, Marco Hutter, Ali-akbar Agha-mohammadi
Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert.
no code implementations • 13 Apr 2022 • Marcel Kaufmann, Robert Trybula, Ryan Stonebraker, Michael Milano, Gustavo J. Correa, Tiago S. Vaquero, Kyohei Otsu, Ali-akbar Agha-mohammadi, Giovanni Beltrame
Real-world deployment of new technology and capabilities can be daunting.
no code implementations • 25 Jul 2021 • David D. Fan, Sharmita Dey, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move.
no code implementations • 4 Mar 2021 • David D. Fan, Kyohei Otsu, Yuki Kubo, Anushri Dixit, Joel Burdick, Ali-akbar Agha-mohammadi
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.
no code implementations • 10 Feb 2021 • Sung-Kyun Kim, Amanda Bouman, Gautam Salhotra, David D. Fan, Kyohei Otsu, Joel Burdick, Ali-akbar Agha-mohammadi
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.
Robotics
no code implementations • 9 Feb 2021 • Kamak Ebadi, Matteo Palieri, Sally Wood, Curtis Padgett, Ali-akbar Agha-mohammadi
Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades.
Loop Closure Detection Simultaneous Localization and Mapping
no code implementations • 26 Jan 2021 • Rohan Thakker, Nikhilesh Alatur, David D. Fan, Jesus Tordesillas, Michael Paton, Kyohei Otsu, Olivier Toupet, Ali-akbar Agha-mohammadi
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages.
no code implementations • 2 Nov 2020 • Kenny Chen, Alexandra Pogue, Brett T. Lopez, Ali-akbar Agha-mohammadi, Ankur Mehta
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems.
no code implementations • 31 Oct 2020 • Yasin Almalioglu, Angel Santamaria-Navarro, Benjamin Morrell, Ali-akbar Agha-mohammadi
In recent years, unsupervised deep learning approaches have received significant attention to estimate the depth and visual odometry (VO) from unlabelled monocular image sequences.
no code implementations • 29 Jun 2020 • Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme
Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments.
no code implementations • 7 Jun 2020 • Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos
This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds.
no code implementations • 5 Feb 2020 • David D. Fan, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
This uncertainty may come from errors in learning (due to a lack of data, for example), or may be inherent to the system.
2 code implementations • 5 Oct 2019 • David D. Fan, Jennifer Nguyen, Rohan Thakker, Nikhilesh Alatur, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties.
no code implementations • 20 Feb 2015 • Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Jonathan P. How
To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP).