Customizable system provides robust positioning without added site infrastructure for IHI Corp.
Trimble has announced the first deployment of its map-based localization system for land-based autonomous vehicle applications.
IHI Corp., a heavy industry manufacturer based in Japan, will retrofit its existing container and haulage trucks with a customized Applanix POS LV system as part of its broader autonomy capabilities for the transport of goods around industrial facilities.
Map-based localization provides precise positioning and orientation estimation, augmenting GNSS/inertial data, which is critical for safe and efficient autonomous vehicle operations. The ability to provide IHI Corp. a full workflow and real-time data ensures seamless integration into IHI’s truck design.
The custom-built, locally supported system leverages Trimble’s engineering capabilities and technology to provide reliable performance across a variety of challenging environments, the company said. Using this system, IHI Corp. can provide robust positioning for its autonomous fleet without additional site infrastructure, lowering capital expenditure costs and improving scalability.
Tailoring POS LV to work within IHI’s unique specifications and existing autonomous platform, the map-based localization system couples an inertial navigation system (INS) with simultaneous localization and mapping-based (SLAM) capabilities, and works with several types of sensors, including lidar. POS LV provides an accurate base map using post-processed data and localizes vehicle positioning in real time, enabling the reliable and safe autonomous operation of industrial vehicles.
IHI continually enhances its work environments, while also compensating for varying labor scenarios and personnel shortages. This makes the need to automate transportation critical to operations. The complexities of the evolving industrial manufacturing environment require solutions that can be tailored to a customer’s specific application requirements.
By partnering with Trimble, IHI can develop a retrofit system that addresses two major challenges — affordability and reliability — within the autonomous operation of large-scale industrial equipment.