Electrical and Computer Engineering, University of Michigan – Dearborn
In the past couple of years, I have been posting news and research updates on LinkedIn:
https://www.linkedin.com/in/srawashdeh/recent-activity/all/
I no longer use the blog side of my website to post updates. The remaining content on this website is relatively up-to-date (publications, biography, published software projects, etc).
We are grateful for the support from the National Science Foundation to acquire this Humanoid Robot to support research and teaching at the University of Michigan – Dearborn. The proposal writing, design, and development involved numerous faculty colleagues and students at UM-Dearborn. We are also grateful for the excellent engineers and designers at Sundberg-Ferar who designed the torso and head and worked on general integration of the robot components.
Over the summer, the robot was completed and we have started developing the software framework to support projects. We are using the Robot Operating System (ROS), and developing the software nodes to control the various components. Also, we are developing a simulation model of the robot to be able to try out algorithms in simulation before deploying them onto the actual (expensive) robot. Below are screenshots and a video of our early efforts developing a perception algorithm to detect objects on a work surface and the robot arm path planning to avoid collisions and move the hands to the object.
A quick note: the robot moves very slowly in the video for safety reasons.
Over the past year, we have built on our visual odometry algorithm (which I shared in an earlier post and made the source code public) to develop a multi-object tracking algorithm capable of tracking objects in the 3D world from the moving camera observer. An example of where this may be useful is for self-driving vehicles to detect moving objects (other cars) that are on a collision course.
We have written this research up in a research paper, which has been accepted this week into the IEEE Sensors Journal. It will take a few weeks for it to appear on IEEE Xplore.
Mohamed Aladem, S. A. Rawashdeh, “A Combined Vision-Based Multiple Object Tracking and Visual Odometry System”, IEEE Sensors Journal. Accepted 8/2019.
Below is a video demo. Credit to Mohamed Aladem, my Ph.D. student who is on track to graduate in mid-2020.
We have recently published the source code of our visual odometry algorithm, which supports stereo and RGB-D camera systems.
Source code is available at: (github link). It is compatible with ROS (Robot Operating System).
Our journal publication was recently accepted, it is available at Sensors. This is the paper’s abstract:
Lightweight Visual Odometry for Autonomous Mobile Robots
Vision-based motion estimation is an effective means for mobile robot localization and is often used in conjunction with other sensors for navigation and path planning. This paper presents a low-overhead real-time ego-motion estimation (visual odometry) system based on either a stereo or RGB-D sensor. The algorithm’s accuracy outperforms typical frame-to-frame approaches by maintaining a limited local map, while requiring significantly less memory and computational power in contrast to using global maps common in full visual SLAM methods. The algorithm is evaluated on common publicly available datasets that span different use-cases and performance is compared to other comparable open-source systems in terms of accuracy, frame rate and memory requirements. This paper accompanies the release of the source code as a modular software package for the robotics community compatible with the Robot Operating System (ROS).
This video highlights the algorithm in action:
Our winter semester at University of Michigan – Dearborn recently concluded. In my ECE473 Embedded System Design course, we work ARM Cortex-M microcontrollers and focus on real-time processing, embedded software architectures, and real-time operating systems.
The following two videos highlight two notable projects from this term:
One team developed a debugging terminal for the uCOS-II RTOS. Debugging RTOS projects can be challenging without being able to observe the state of tasks and shared resources. The team put together this video.
Another team developed their own cooperative RTOS for the TI TM4C123 Microcontroller (ARM Cortex-M4 core). The team put together this video.
I’m pleased to share another demonstration video of our stereo visual odometry algorithm, primarily developed by my student Mohamed Aladem who is wrapping up his master’s at the University of Michigan – Dearborn. Near term goals for our lab using this framework are: navigating mobile robots (namely an autonomous snowplow for the ION Autonomous Snowplow Competition – see previous post to this one), navigating a multi-copter, and explore solutions for automotive driver assistance systems and future autonomous vehicles.
Publications:
I’m pleased to share another demonstration video of our stereo visual odometry algorithm, primarily developed by my student Mohamed Aladem who is wrapping up his master’s at the University of Michigan – Dearborn. Near term goals for our lab using this framework are: navigating mobile robots (namely an autonomous snowplow for the ION Autonomous Snowplow Competition – see previous post to this one), navigating a multi-copter, and explore solutions for automotive driver assistance systems and future autonomous vehicles.
Publications:
We have some exciting results. Below is a brief demonstration of our recent success with visual odometry. Using stereo cameras, the camera motion and pose are tracked over time, along with depth sensing (stereo disparity map) and point cloud generation.
Publications, currently in preparation and expected this summer, will discuss our approach.
I am happy to share that we had a good run at the Intelligent Ground Vehicle Competition this weekend which took place at the Oakland University campus.
The vehicle uses a LIDAR system for obstacle avoidance, GPS for Navigation and real-time image processing for lane detection.
Of over 30 teams participating, the Dearborn team came in third overall, with 3rd fastest speed on the basic navigation course, and tying on the advanced navigation course in terms of performance but at a longer time, earning 2nd place. Below are some photos and a short video of what the advanced course is like. Prizes total $3k and a trophy.
The team of primarily undergraduate students from the Intelligent Systems Club did very well and deserve thanks and congratulations. The team inclued Michael Bowyer, Erik Aitken, Saad Pandit, Cristian Adam, Matthew Abraham, Siddharth Mahimkar, Emmanuel Obi, Brendan Ferracciolo, Angelo Bertani, and others from the club.
