Description

• Processes video streams or static images in real time.
• A semantic segmentation model (based on machine learning) identifies relevant object classes – such as pallets, loads, transport corridors, vehicles, and people – and their precise positions in each frame.
• The segmented images can be enriched with facility infrastructure data to enable real-time monitoring, e.g., detecting loaded or empty pallet positions, misplaced items, or equipment
• Depth integration: by fusing data from depth cameras or LiDAR, the system can accurately recognize the position and shape of pallets and detect obstacles in the transport areas enabling advanced functionalities, such as obstacle-specific avoidance behavior (e.g., avoid static obstacles, stop near persons) or adaptive pickup of imprecisely placed pallets.
• The model is trained on intralogistics-specific datasets, ensuring robust performance in warehouse environments.
• Can be fine-tuned for specific facilities, further improving site-specific precision and adaptability.

Innovations

The technology gives machines human-like environmental understanding. Unlike traditional vision systems limited to fixed object templates, it recognizes and distinguishes between people, pallets, loads, vehicles, and navigable terrain – unlocking new capabilities such as adaptive load handling, obstacle avoidance, and real-time facility-level analytics.

Key innovations include:

• Generic object perception, independent of pallet or load type.
• Automated camera-fork calibration for load handling (EP24223684.2 patent pending).
• Real-time operation, up to 20 fps.

Together, these features enable flexible, vision-driven automation that dramatically enhances AGV/AMR performance and facility visibility.

Market relevance

The innovation primarily targets AGV and AMR manufacturers as well as system integrators. By integrating our semantic perception modules, they can offer safer, more autonomous, and more efficient vehicles to end users in manufacturing and logistics.

The combined AGV/AMR market is estimated at about 70,000 units sold per year in 2024, and expected to grow to 110,000 units by 2030. With the expected target price for a semantic perception software package ranging between €1,000 and €5,000, the estimated market potential today is between €70 million and €350 million, and expected to grow by about 50 % by 2030.

A secondary market includes end users who can deploy the software on existing camera infrastructure to gain real-time insights into asset utilization and material flow. This segment could exceed the primary market in value as facilities increasingly pursue digital twins and AI-driven analytics.

Customer benefits

The system increases operational transparency, efficiency, and safety within intralogistics environments through the following specific benefits:

• Real-time visibility of facility assets and material flows.
• Reduced downtime by early detection of obstacles and irregularities in transport corridors.
• Simplified handovers between workers and AGVs/AMRs through contextual scene understanding.
• Automated handling of empty pallets, including collection, stacking, and destacking.
• Continuous monitoring of irregular conditions such as misplaced items or equipment.
• Reduced deployment time and integration complexity of AGV/AMR systems due to increased environmental awareness of the vehicles.