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T52

ILS-T52 3D Depth Imaging LiDAR

T52

Dedicated 3D depth camera for unmanned forklifts—125° ultra-wide angle, precisely locks onto the fork tip’s field of view.

The T52 is an industrial-grade 3D Time-of-Flight depth camera specifically designed for unmanned forklifts and heavy-duty AGVs. It features an ultra-wide horizontal field of view of 125° × 55°, enabling it to perfectly cover the broad area in front of the forklift and completely eliminating blind spots on both sides of the fork tips and chassis.
The T52 provides high-resolution depth data at 640 × 240, and thanks to its optimized optical design, it can precisely detect pallet hole positions, shelf beams, and ground obstacles. Its unique dual-module design, combined with a light resistance capability of 10,000 lux, ensures stable operation even in warehouses with alternating light conditions. Whether it’s the precise insertion and retrieval of items from high-level stacks or the sensitive obstacle avoidance in narrow aisles, the T52 is an indispensable sensing hub for unmanned forklifts.

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Overview

125° ultra-wide angle, with a dead-angle-free field of view.

Compared to conventional 3D cameras, the T52’s horizontal field of view has been increased to: 125° This groundbreaking design enables a single device to cover the entire front-end operating area of an unmanned forklift, allowing it not only to clearly see the pallet directly in front but also to take into account obstacles along both side legs and at the edge of the chassis.

High-resolution imaging enables more accurate pallet recognition.

Output 640 × 240 Its high-density point cloud has twice the resolution of the T51. This means that the T52 can capture richer object details, helping algorithms accurately identify the center of pallet hole positions from greater distances and significantly improving the success rate of automated loading and unloading operations for unmanned forklifts.

Optimized specifically for forklift operating conditions—unafraid of vibration and strong light.

Pure solid-state design with no mechanical components, perfectly adapted to the high-frequency vibrations encountered during forklift operation. The optimized optical system features... 10,000 Lux Strong resistance to light—maintains stable depth measurement even in areas directly exposed to sunlight, such as near warehouse doors and windows, without losing the target.

Core features

Imaging technology: 3D ToF (Time-of-Flight)
Ultra-wide field of view: 125° (horizontal) × 55° (vertical)
High resolution: 640 × 240 pixels
Measurement range: 0.05 m ~ 3 m (10% reflectance)
Frame rate: 15 fps
Measurement accuracy: ±2 cm (0.15 m ~ 1 m)
Obstacle avoidance function: 16 regional group switches
Data interface: 100M Ethernet (TCP/IP)

Defining the “Golden Vision” for Unmanned Forklifts

When operating, an unmanned forklift must not only clearly identify the pallet slots directly ahead to ensure precise docking but also pay close attention to whether the side outriggers might collide with the racking. Traditional narrow-field-of-view cameras often fail to balance these two critical tasks effectively.

The T52, with its 125° golden field of view, perfectly resolves this contradiction. Mounted between the forks or at the front of the vehicle, it can simultaneously monitor both the core operational area and the surrounding safety zone, ensuring that every lift and every turn of the forklift is handled with ease and precision.

Made for pallet handling

Accurate identification, hitting the mark every time.

The high-resolution depth images provided by T52 can clearly restore the geometric features of the pallet. Combined with the vision algorithm library provided by YINTAILI, you can easily achieve... With a U-shaped base, nine legs, and double-sided. Automatic recognition and pose estimation of various pallet hole positions.

Even if the pallets are improperly positioned or have a certain angular deviation, the T52 can still provide accurate correction data, guiding the forklift to adjust its fork-entry angle and significantly improving the operational efficiency of automated logistics.

Wide field of view, eliminating lateral blind spots.

For wide-body forklifts or heavy-duty AGVs, the blind spot on the side and front is a high-risk area for collisions. T52’s 125° The field of view can cover areas beyond the width of the vehicle.

This means that when a forklift is turning or driving diagonally in a narrow aisle, the T52 can detect shelf uprights, wall corners, or pedestrians ahead and to the side in advance, promptly issuing an obstacle-avoidance signal and completely eliminating the safety hazard of "focusing on the front while ignoring the rear."

Spatial perception—ignore suspended traps.

Traditional 2D bottom radars cannot detect overhead obstacles (such as protruding steel pipes or unreturned drawers). The T52, however, creates a three-dimensional conical detection field.

Whether it’s low obstacles on the ground or suspended objects halfway up, as long as they come within T52’s field of view, they’ll be precisely detected. This provides forklifts with true 3D safety protection, preventing costly damage to both goods and equipment.

Rich interfaces, simpler development

T52 supports standard Ethernet communication and provides C++ / Python SDK and comprehensive ROS / ROS2 Driver Package 。

Not only does it support outputting raw depth maps and point cloud data, but it also allows running obstacle-avoidance algorithms directly within the camera itself and outputs control signals directly via I/O interfaces. Whether used as a visual sensor for secondary development or as a standalone obstacle-avoidance module, the T52 can handle the task with ease.

YINTAILI All-round Services

Visual Algorithm Consulting

Having trouble with pallet recognition? Our algorithm experts are here to provide you with image-processing recommendations and solutions based on T52 point cloud data.

ROS Integration Support

Provides comprehensive guidance on ROS node configuration to help you quickly integrate the T52 into your robot’s navigation system.

Prototype Trial Service

We offer a T52 prototype loan-testing service, allowing you to experience its outstanding performance firsthand in a real warehouse environment.

Explore more potential applications

3D obstacle avoidance and pallet positioning for unmanned forklift forks

When performing cargo storage and retrieval tasks, forklifts often need to extend their forks deep into the shelves or operate in mid-air. Traditional 2D LiDAR sensors, typically mounted at the bottom of the vehicle body, are unable to detect overhead obstacles at fork height—such as protruding steel beams or improperly arranged goods. As a result, forklifts are highly susceptible to “high-altitude collisions” when lifting or moving forward. Moreover, accurately identifying pallet slot positions has long been a persistent challenge in the industry. Relying solely on mechanical positioning is often insufficiently precise, easily leading to failed insertion attempts or even damage to the goods.

Autonomous Forklift: Lateral Clearance Monitoring and Collision Avoidance in Narrow Aisles

In automated stereoscopic warehouses pursuing high-density storage, rack aisles are typically designed to be extremely narrow (VNA). When unmanned forklifts travel at high speeds within these aisles, even a slight deviation or an unexpected protrusion of goods from the racks into the aisle can easily lead to accidents such as goods scraping against racks or the forklift becoming stuck. Traditional single-point ranging sensors can only detect fixed points and cannot cover the entire three-dimensional space along the sides, making it difficult to identify irregularly protruding obstacles.

Autonomous Forklift: Precision Forking Assistance and Pallet Hole Position Recognition

In the entire operational process of unmanned forklifts, “precise fork positioning” is the stage with the highest failure rate. Due to uneven ground surfaces, deviations in pallet orientation, or deformation of goods, relying solely on vehicle navigation and positioning often fails to ensure perfect alignment between the forklift forks and the pallet’s hole positions. Once the deviation exceeds a certain threshold, the forks may collide with the pallet’s upright posts or even topple the goods, leading to serious logistics accidents. Traditional mechanical limit switches or 2D radar systems struggle to capture depth information about the pallet’s cross-section, making it impossible for them to handle pallets with varying orientations.

Autonomous Forklift: Cargo Posture Monitoring and 3D Pathway Protection

When unmanned forklifts are transporting goods at high speeds, they face a dual risk: First, there’s the safety of the cargo itself—emergency stops or sharp turns could cause stacked goods to tilt, slide, or even collapse. Second, there’s the issue of blind spots: Stacked goods often obstruct the line of sight of sensors located beneath the vehicle body, causing the forklift to “fail to see” low-lying obstacles or suspended objects directly in front of it. If the forklift cannot实时 detect the status of the cargo and identify blind-spot risks in real time, it will easily lead to severe damage to the goods or collision accidents.

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Images	Order Number	Product line	Product Name	Product abbreviation	Configuration Instructions	Scanning angle	Scanning frequency	Resolution	Measure the distance	10% reflectivity detection range	Accuracy	Input/Output Interface	Anti-light interference	Operating temperature	Power supply voltage	High and low voltage levels	Power consumption	Protection	Housing dimensions	Shell material	Cable length	Applicable scenarios	资料下载	询价	Contrast
	0101236	T52	ILS-T52 3D Depth Imaging LiDAR	T52	Fork tip obstacle avoidance	120°×55°	10 Hz	640*240	3 meters	3 meters	±2 cm	Ethernet, multi-channel IO level output	100,000 Lux	-10°C to 50°C	DC 10~28V	-	≤4W	IP65	80mm (length) × 95mm (width) × 20mm (height)	Aluminum alloy	-	Fork tip obstacle avoidance