Autonomous Forklifts
Overview
In autonomous forklift applications, 3D cameras provide stable 3D perception. For uniformly stacked loads and standardized storage locations, they identify pallet pose and storage-location position for accurate, efficient fork engagement. In complex warehouse environments, point-cloud data reconstructs the geometry of loads, racks, and obstacles to support real-time route planning and collision avoidance.
Key Challenges
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Worn, reflective pallet and load surfaces, along with inconsistent markings, complicate recognition -
Dense loads, narrow aisles, and occlusion impair fork-position localization -
Uneven lighting and dust degrade environmental perception accuracy
Key Advantages
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Builds a complete 3D representation of warehouse areas for accurate pallet and storage-location localization
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Recognizes load-stack geometry to support fork positioning and travel path planning
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Delivers robust operation in dusty warehouses and demanding continuous-duty conditions
Recommended cameras
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GM461-E1A cost-effective 3D industrial camera with a 100 Mbps Ethernet interface that combines industrial-grade stability with consumer-grade pricing. Highly versatile, it is widely used in various fields such as manufacturing, logistics, and robotics.
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TM262-E1dToF depth-sensing camera: offering excellent value for money, an ultra-wide field of view and resistance to sunlight interference, with industrial-grade stability and a compact design, suitable for a wide range of indoor and outdoor applications.
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TM265-E13D dToF LiDAR, with millimetre-level depth accuracy, resistance to strong light, a detection range of up to 5 metres and an ultra-wide field of view, is suitable for industrial and logistics applications such as navigation and obstacle avoidance in mobile robots, as well as people counting.
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