Jordan Liss

Jordan Liss

Mentor
Rising Codementor
US$10.00
For every 15 mins
ABOUT ME
Classically trained roboticists with passion for code mentoring
Classically trained roboticists with passion for code mentoring

Passion for working on autonomous systems from the ocean to space and all in between. I am trained as a computer engineer with a focus on robotic systems, controls, computer vision, system identification, sensor fusion, attitude estimation and system integration. I’m dedicated to creative problem solving. Worked in a wide variety of fields in my engineering career from clean energy to transportation to airborne science research to CubeSats to factory automation to ocean robotics.

Pacific Time (US & Canada) (-07:00)
Joined October 2023
EXPERTISE
10 years experience
7 years experience
3 years experience
6 years experience
5 years experience
5 years experience
10 years experience

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SOCIAL PRESENCE
GitHub
om_python_motor_software
Python
1
0
OfficeScripts
Useful office scripts that will exponentially improve your workflow production with codes to read scanned documents, automate document organization and create files with document metadata.
Python
1
0
EMPLOYMENTS
Self-employed
Self-employed
2023-09-01-Present

Everything robotics

Everything robotics

Python
C++
C
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Python
C++
C
Bash
OpenCV
OpenGL
Network
MATLAB
ROS
Embedded Systems
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Robotics Engineer
Greensea Systems, Inc.
2022-07-01-2023-10-01
  • Develop, test, and implement doppler velocity logger (DVL) C++ application driver for boat hull cleaning ROV navigation system.<...
  • Develop, test, and implement doppler velocity logger (DVL) C++ application driver for boat hull cleaning ROV navigation system.
  • Design and develop automated gyro alignment software in C++ using compass (magnetometer), and GPS.
  • Design, develop, and integrate feedback-forward controller of ROV with robotic arms using ROS/LCM middleware.
  • Design, implement, and optimize camera calibration and sonar perception software for SLAM using amphibious ocean vehicles(A2RVs).
  • Develop behavior tree based autonomous driving software for ROVs/A2RVs in C++.
  • Develop and implement Kalman/Extended Kalman filters sensor fusion and motion planning software in C++.
  • Lead team of developers to test and integrate applications on vehicle with multiple distributed x86 and ARM processors leveraging LCM middleware.
Python
C++
C
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Python
C++
C
Linux
Bash
OpenCV
Image Processing
GPS
Computer Vision
GPU
Wireshark
MATLAB
Rust
ROS
Control Systems
Sensor Fusion
Robotics Engineering
Wireless networks
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Project Engineer
Quartus Engineering, Inc.
2018-07-01-2022-07-01
  • Develop pre-camera calibration image processing IQ/VQ algorithms in MATLAB/Python/C++ to deploy on RTOS factory instrumentation....
  • Develop pre-camera calibration image processing IQ/VQ algorithms in MATLAB/Python/C++ to deploy on RTOS factory instrumentation.
  • Develop closed-loop camera auto-exposure, auto-focus, auto-white balance algorithms in Matlab/Python/C++ for camera test instrumentation.
  • Design and build highly accurate factory camera calibration process leveraging robotic arms (i.e. UR5), optical based fiducial systems, computer vision, Lua/C#/Obj-C camera plugins, camera models, coordinate transformations, and non-linear model optimization.
  • Design, build, and optimize camera calibration processes using computer vision, image processing, and 6DOF coordinate transforms.
  • Design, build and test compact highly precise optical based intrinsic and extrinsic camera calibration station for factory IQC/OQC, and MP.
  • Develop ObjC/Swift OSX applications for motor control, laser hardware, temperature control, cameras and laboratory instrumentation. Build flash imaging LiDAR factory quality control stations and build hardware integration software in Lua/Swift/Obj-C and station GUI development in Swift.
Bash
Image Processing
Lua
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Bash
Image Processing
Lua
Computer Vision
MATLAB
Objective-C
Swift
.NET Core
.NET
Motor Control
Robotic Process Automation
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PROJECTS
Master’s Thesis – “Verification of TRIAD and Complementary Filter on Cubesat Using Low-Cost Extrinsic Visual Marker Tracking with EKF”View Project
2018
Survey of nanosat attitude determination, and DCM based feedback controllers using low-cost IMU sensors and solar photodiodes at NASA Ame...
Survey of nanosat attitude determination, and DCM based feedback controllers using low-cost IMU sensors and solar photodiodes at NASA Ames Research Center verified by external reference with OpenCV AprilTag software package and multiple high-definition web-cams.  Developed computer vision motion tracking system in C++ using OpenCV AprilTag and inexpensive high definition webcams.  Test, compare and verify satellite attitude determination algorithms1 in MATLAB/Simulink/ C++ using IMUs/MARGs and photodiodes sensors on CubeSats with board-level computers (i.e. Beagle Bone Black)  Implement real-time DCM based feedback controller in C++ on CubeSat equipped with 4 magnetic torque rods.
C++
OpenCV
MATLAB
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C++
OpenCV
MATLAB
Simulink
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