Pedestrian-Following Service Robots Made Possible with CUDA Acceleration

A team of researchers from Seoul National University built a pedestrian-following service robot to drive smart shopping carts and other autonomous helpers. The performance of their initial CPU-only implementation was “not acceptable for a real-time system” so they now use a GPU-accelerated CUDA implementation for a 13x performance boost.

Mobile robots that track a person have received considerable attention for a number of applications such as transporting loads, and serving customers – but in general, the robots have limited range sensors.

Pedestrian Tracking Robot
The smart shopping cart robot application. The person with a blue shirt is a target and a mobile robot with a gray basket is a shopping cart.
The tracking algorithm created by the team minimizes both the probability of losing a target and traveling distance of the target. By considering the distribution of predicted position of a target, they get robust performance against the uncertainty of prediction.

Besides the shopping cart, the researchers tested the operating system on a selfie robot (which can also be used as a care robot for seniors) and a transporter robot.

Read the research paper >>

About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter