Humanoid robot is here

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.




A Combined Vision-Based Multiple Object Tracking and Visual Odometry System

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. 

Lightweight Visual Tracking (LVT): source code and paper published

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:


Winter 2018 – ECE473 Course Projects

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:

RTOS System Monitor and Debugging Terminal for uCOS-II

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.

BLuE RTOS – Cooperative RTOS for Cortex-M4 (TI Tiva Launchpad, TM4C123)

Another team developed their own cooperative RTOS for the TI TM4C123 Microcontroller (ARM Cortex-M4 core). The team put together this video.

Demonstration of a Stereo Visual Odometry Algorithm

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.


  • Mohamed Aladem, Samir Rawashdeh, Nathir Rawashdeh, “Evaluation of a Stereo Visual Odometry Algorithm for Road Vehicle Navigation”, SAE World Congress, April 2017 Detroit, MI
  • S. A. Rawashdeh, M. Aladem, “Toward Autonomous Stereo-Vision Control of Micro Aerial Vehicles”, Proceedings of the IEEE National Aerospace and Electronics Conference, July 2016, Dayton, OH
  • Journal article pending.


2017 Autonomous Snowplow Competition

At the 2017 ION Autonomous Snowplow Competition, of 8 competing teams UM-Dearborn’s Yeti won second place ($4000) and team Zenith won first place in the new Cooperative Snowplow challenge ($700). 
Yeti and Zenith are primarily developed by students from the Intelligent Systems Club (ISC), advised by prof. Rawashdeh. Yeti uses a LIDAR for localization and obstacle avoidance, while Zenith is based on stereo vision. 
Some photos and videos can be found on the ISC club’s Twitter page.


Stereo Visual Odometry

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.

3rd Place at IGVC 2016 (Intelligent Ground Vehicle Competition)

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.


Presenting at IEEE Southeast Michigan 2015 Fall Conference

We are excited to be presenting our research on Obstacle Avoidance for Drones at the IEEE SEM 2015 Fall Conference, 5-6PM, Nov 17. The talk is titled

“Obstacle Detect, Sense, and Avoid for Unmanned Aerial Systems”


Drones, or Unmanned Aerial Systems (UAS), are expected to be adopted for a wide range of commercial applications and become an aspect of everyday life. The Federal Aviation Administration (FAA) regulates airspace access of unmanned systems and has put forward a road map for UAS adoption for commercial use. It is expected that vehicles flying outside line-of-sight be capable of sensing and avoiding other aircraft and obstacles. Whether the UAS is autonomous or remotely piloted, it is expected that drones become capable of safe flight without depending on communication links which are susceptible. Therefore, sensor technologies and real-time processing and control approaches are required on board unmanned aircraft to provide situational awareness without depending on remote operation or inter-aircraft communication. This talk overviews some research activities at the University of Michigan Dearborn to address this challenges. We are developing a stereo-vision system for obstacle detection on aerial vehicles. Using stereo video (3D video), a depth map can be generated and used to detect approaching objects that need to be avoided. We are also developing a visual navigation approach to enable drones to navigate in GPS denied environments, such as between buildings or indoors. Also, a virtual “bumper” system is being developed to over-ride commands being given by an in-experienced pilot in the case of an impending crash. Such a system could help prevent incidences such as the video drone crash at the last US Open Tennis Championships.

IEEE SEM Fall Conf 2015 - Slide Screensho

Conference Time and Venue:

Tuesday Evening, November 17, 2015, From 4:00 PM to 9:00 PM

University of Michigan – Dearborn
Fairlane Center – North Building
19000 Hubbard Drive, Dearborn,
Michigan 48126

More information on conference agenda can be found at the main page, and in this flyer.