Johan Steenkamp

Johan Steenkamp

Software Engineer @ Schauenburg Systems

About

At IOB.inc, my role as an Engineer in Edge AI involves leveraging my Master's in Artificial Intelligence to innovate embedded systems. We're focused on developing solutions that seamlessly integrate AI with sensor networks, enhancing real-world technology applications. The skills gained from my Engineer's degree at the University of Pretoria and the advanced knowledge from the University of the Witwatersrand drive my contributions to a future where technology is not just advanced but also life-enriching. With a passion for point clouds and a knack for navigating complex sensor networks, my work supports the pioneering spirit at IOB.inc.

Country

South Africa

City

City of Johannesburg

Industry

Information Technology & Services

Skill

Machine Learning, REST APIs, Point Clouds, Sensor Networks, GitHub, Mqtt, Microcontrollers, Deep Neural Networks (DNN), Internet of Things (IoT), Firmware, 3D Tracking, Radar, Real-Time Operating Systems (RTOS), Embedded Software, Embedded Software Programming, Systems Engineering, Control Systems, ARM Cortex-M, Embedded Linux, Conversational AI

Experience

Schauenburg Systems

Software Engineer

Schauenburg Systems

LinkedIn
2025-7 - Present · 1 yr 3 mos
The Internet of Behaviors Company

Engineer : Edge AI

The Internet of Behaviors Company

LinkedIn
2025-3 - 2025-8 · 6 mos
The Internet of Behaviors Company

Junior Engineer : Edge AI

The Internet of Behaviors Company

LinkedIn
2024-3 - 2025-8 · 1 yr 6 mos

South Africa

Education

University of the Witwatersrand

University of the Witwatersrand

LinkedIn

Artificial Intelligence

2022-5 - 2024-1 · 1 yr 9 mos

My masters focused on evaluating vision-based localization methods in challenging subterranean environments. A novel dataset was created using an autonomous exploration UAV within a simulated environment (see attached media). The study involved modifying the ORB-SLAM3 platform to include a variety of feature extraction methods, especially those leveraging deep learning to enhance feature extraction and description. The application of these methods in subterranean scenarios was compared to Lidar-based methods. Monocular methods were found to be limited primarily due to their inability to accurately perceive the scale of the environment, while RGB-D sensor configurations offered some advantages in confined spaces but were less effective in larger areas.

University of Pretoria

University of Pretoria

LinkedIn

Computer Engineering

2018-1 - 2021-12 · 4 yrs

Johan Steenkamp's Contact Information

Email

******@***.com

Phone

(**) *** ****

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