Olivia Christie

Electrical engineering

Hometown: Mesa, Arizona, United States

Graduation date: Spring 2021

Data icon, disabled. Four grey bars arranged like a vertical bar chart.

FURI | Spring 2020

Analyzing Sensor Quantization of RAW Images for Visual SLAM

Visual simultaneous localization and mapping (SLAM) is an emerging technology that enables low-power devices with a single camera to perform robotic navigation. Most visual SLAM algorithms are tuned for images produced through the image sensor processing (ISP) pipeline optimized for highly aesthetic photography. We investigate the feasibility of varying sensor quantization on RAW images directly from the sensor to save energy for visual SLAM. An 88% energy savings has been achieved by decreasing quantization bit level to five bits. We also introduce a gradient-based quantization scheme that increases energy savings. This work opens a new direction in energy-efficient image sensing for SLAM.


QR code for the current page

It’s hip to be square.

Students presenting projects at the Fulton Forge Student Research Expo are encouraged to download this personal QR code and include it within your poster. This allows expo attendees to explore more about your project and about you in the future. 

Right click the image to save it to your computer.

Additional projects from this student

Researching energy-efficient autonomous algorithms will enable a robot to map heat-related threats in the environment.


  • FURI
  • Fall 2019