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Research and development of next-generation autonomous systems that can safely operate in a range of work environments
Industry: Warehousing / Manufacturing
Use Case: Autonomous Robots for Material Handling
Location: Ontario, Canada
Scale 3D Sensor Fusion & Cuboid Annotation
Scale Image & Semantic Segmentation Annotation
About OTTO Motors
OTTO Motors is an Ontario-based company that specializes in developing flexible and intelligent mobile robots for automated materials handling in manufacturing facilities and warehouses around the world. OTTO’s industrial-grade platform moves goods intelligently through facilities when and where they are needed, streamlining production while safely avoiding pedestrians. OTTO robots operate safely and efficiently in a wide variety of environments. “One of our biggest initiatives is to make sure our robots are reliable and accurate; ensuring they function effectively in the various environments where they are being deployed,” said James Servos, Autonomy Engineering Manager at OTTO Motors.
Developing Datasets from Scratch
To research and develop next-generation systems that can better understand, navigate, and safely operate in a range of work environments, the OTTO team needed data from a variety of diverse environments. At the time the OTTO team first began working on this problem, most publicly available datasets for autonomous mobility were specifically for autonomous road vehicles like cars and trucks. “For what we wanted to do, there was no publicly available data,” said Nolan Lunscher, Senior Autonomy Developer at OTTO. “We had to start from scratch on getting datasets and we needed a partner who could supply us with a steady supply of data in a domain that was quite different from what everyone else was working on,” he added.
“We had to start from scratch on getting datasets and we needed a partner who could supply us with a steady supply of data in a domain that was quite different from what everyone else was working on.”
Senior Autonomy Developer, OTTO Motors
Throughput and Volume OTTO Can Count On
The OTTO team selected Scale AI as its data annotation provider for a few reasons. First, Scale AI’s ability to annotate a range of data types from 3D sensor data to camera imagery gave the OTTO team confidence in Scale AI to handle the kinds of data they had. Second, Scale AI’s experience in the autonomous driving space meant Scale AI could help the OTTO team better understand possible edge cases. “The On-Demand Service also gave us a good opportunity to trial the system to get a feel for how the process works and how the tools work,” said Lunscher. “Once we had a feel for it, we were able to jump in with our larger project,” he noted.
“The cost of developing all of the tools and workflows to annotate data for a company like us was just completely not feasible,” Servos added. “The Scale AI team was able to annotate our data at a pace far above what we would have expected while keeping the overhead for data annotation reasonable,” he concluded.
The Future of Warehousing and Manufacturing
By working with Scale AI, Servos and his team are able to focus on their actual work of improving its systems rather than on data annotation. “Someone like [Lunscher] is an incredibly high-value worker as an autonomy engineer. Working with Scale means keeping him focused on developing cutting edge algorithms and state of the art networks,” Servos noted.
The diversity of environments and areas OTTO’s systems must operate in is vast. “Every time we deploy to a new customer site, we find new corner cases. We’ll continue to refine our systems and push more data through Scale’s labeling pipeline to develop generalized networks that can function in all of these locations,” Servos concluded.
“The cost of developing all of the tools and workflows to annotate data for a company like us was just completely not feasible. The Scale AI team was able to annotate our data at a pace far above what we would have expected while keeping the overhead for data annotation reasonable.”
Autonomy Engineering Manager, OTTO Motors