Self-driving startup Ouster nears $1.9 billion deal to go public: Sources1 min read . Updated: 22 Dec 2020, 10:32 AM IST
San Francisco-based Ouster plans to merge with Colonnade Acquisition Corp, which would make Ouster the fifth lidar manufacturer this year to agree a SPAC merger to go public.
- Valuation of the deal through the merger to go public to be near $1.9 billion.
Ouster Inc, a US startup that makes lidar sensors for self-driving cars and smart cities, is nearing a deal to go public at a roughly $1.9 billion valuation through a merger with a blank-check acquisition firm, according to people familiar with the matter.
San Francisco-based Ouster plans to merge with Colonnade Acquisition Corp and the deal could be announced as early as Tuesday, the sources said, requesting anonymity ahead of an official announcement.
Colonnade is a special purpose acquisition company (SPAC) led by investors Remy Trafelet and Joseph Sambuco. It raised $200 million in an initial public offering (IPO) in August with the aim of merging with a privately held company. The acquired company then becomes public as result of the merger, an alternative to the traditional IPO process.
A tie-up with Colonnade would make Ouster the fifth lidar manufacturer this year to agree a SPAC merger to go public, following on from Velodyne Lidar Inc, Luminar, Innoviz and Aeva.
Lidar sensors, which use laser light pulses to render precise images of the environment around the car, are seen as essential by many automakers to allow higher levels of driver assistance, right up to making them capable of self-driving.
Ouster's management has talked about the broader applications for lidar in areas such as drones, smart cities and robotics, and not just for autonomous vehicles.
Five-year-old Ouster previously raised $142 million from private market investors, including Cox Automotive, Silicon Valley Bank and Fontinalis Partners, which is co-owned by Ford Motor Executive Chairman Bill Ford.
This story has been published from a wire agency feed without modifications to the text.