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T53

ILS-T53 3D Depth Imaging LiDAR

T53

Pure Data-Driven 3D Depth Camera—Minimal Integration, Focused on Empowering Point Cloud Data

The T53 is a pure-data 3D Time-of-Flight depth camera designed specifically for users engaged in secondary development. It eliminates complex I/O interfaces and focuses exclusively on the acquisition and transmission of high-quality 3D data. Through a high-speed Ethernet interface, the T53 can output real-time depth maps, grayscale images, and point cloud data at a resolution of 320×240.
With its compact solid-state design, a wide field of view of 72° × 55°, and excellent resistance to ambient light, the T53 is an ideal data source for mobile robots performing SLAM mapping, object recognition, and advanced obstacle-avoidance algorithms. For developers who have powerful host-computer computing capabilities and are seeking a minimalist hardware architecture, the T53 represents the most cost-effective choice for 3D perception.

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Overview

Pure data, minimalist integration

The T53 is designed specifically for users developing applications based on PCs or embedded controllers (such as Jetson and Raspberry Pi). It eliminates unnecessary I/O lines on-site, retaining only a high-speed Ethernet interface, thereby simplifying hardware integration and enabling developers to focus entirely on the algorithm itself.

Enrich data, empower intelligent algorithms

Real-time output Depth Map (Depth) 、 Grayscale image (IR) And Point Cloud Data. The rich three-dimensional information provides a high-quality input source for advanced algorithms such as SLAM navigation, object recognition, and volume measurement.

Industrial-grade quality, ultimate cost-effectiveness.

Continuing the all-solid-state, industrial-grade design of the T series, it features... IP65 It features a high level of protection and excellent resistance to light interference. By streamlining unnecessary functions, the T53 delivers uncompromised 3D imaging quality at a more competitive price.

Core features

Imaging technology: 3D ToF (Time-of-Flight)
Data output: Depth map, grayscale image, point cloud
Field of view angle: 72° (horizontal) × 55° (vertical)
Resolution: 320 × 240 pixels
Measurement range: 0.05 m ~ 3 m (10% reflectance)
Frame rate: 15 fps
Data interface: 100M Ethernet (TCP/IP)
Size: 76mm × 25mm × 25mm (more compact)

The Developer’s Ideal Choice: Data Empowerment with T53

For developers of intelligent robots and machine vision systems, the T53 is a pure and powerful data acquisition terminal. Like an tireless eye, it continuously digitizes depth information from the 3D world and transmits it to the “brain” via standard Ethernet protocols. Whether it’s mobile robot navigation based on ROS or object recognition systems powered by deep learning, the T53 can provide the most fundamental and core perceptual data support.

Advantage: Streamlined, yet not simplistic.

Direct connection at the front end, plug-and-play.

The T53 eliminates complex I/O wiring harnesses, retaining only a single composite cable (or standard aviation connector) that integrates power supply and data transmission.

This design greatly simplifies the robot’s internal wiring and reduces the risk of electrical connection failures. Simply plug it into a switch or the network port of an industrial PC, and you can start receiving data—making hardware integration cleaner than ever before.

Software-defined obstacle avoidance, with more flexible strategies.

Unlike the F31-C, which hardwires obstacle-avoidance logic into the radar itself, the T53 returns decision-making authority to the host computer.

You can leverage the real-time point cloud uploaded by T53 to run more sophisticated obstacle-avoidance algorithms—such as the Dynamic Window Approach (DWA) and local path planning—in the navigation controller. This means that obstacle-avoidance strategies are no longer limited to fixed “stop/slowdown” zones; instead, they can achieve more advanced, intelligent behaviors like bypassing obstacles and following paths.

3D feature extraction for more accurate recognition.

The depth data provided by T53 contains the geometric features of objects. Using this data, developers can easily train algorithms to recognize specific objects, such as: Identify pallet hole positions to assist with forklift entry, recognize cargo container volumes to optimize loading, and detect human body postures for safe interaction. 。

This enables the T53’s application scenarios to go far beyond simple “obstacle avoidance,” making it a cost-effective sensor for the industrial vision field.

Smaller size, more possibilities.

The T53’s size has been further reduced to 76mm × 25mm × 25mm , weighing only 90g.

Such extreme compactness allows it to be installed in tiny spaces where conventional sensors simply cannot fit, such as: End effector of a robotic arm, underside of a drone, head of a small service robot Wait—this provides great freedom in device design.

YINTAILI All-round Services

SDK and Driver Support

We provide C++/Python SDKs and standard ROS drivers, along with a wealth of data acquisition and parsing examples to help you quickly get started with development.

Developer community support

Facing development challenges? Our technical support team offers code-level consulting services to help you resolve issues related to data integration and algorithm adaptation.

Prototype Trial Service

We provide convenient channels for purchasing and trying out samples, tailored specifically for R&D departments at universities, research institutions, and enterprises, to support your innovation validation efforts.

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: 3D Obstacle Avoidance and Spatial Safety Protection

When operating among densely packed storage racks, unmanned forklifts face complex three-dimensional spatial challenges. Traditional 2D obstacle-detection radars can only scan a plane approximately 20 cm above the ground, leaving a significant vertical blind zone. As a result, these radars often “fail to see” overhead rack beams, partially protruding goods, or low-level pallets on the floor. This makes forklifts highly susceptible to accidents during operation—such as collisions between the mast and overhead objects or damage to goods from rubbing against surrounding facilities. Such incidents not only cause costly damage to logistics equipment but also pose a serious threat to the safety of personnel on site.

Intelligent Warehouse Robots (AGVs/AMRs): 3D Environment Perception and Stereoscopic Obstacle Avoidance

In automated warehouses with dense storage, AGVs and AMRs need to swiftly navigate through narrow aisleways between shelves. Traditional 2D LiDAR sensors can only scan a single plane at a fixed height above the ground—typically around 20 cm—leaving significant vertical blind spots. As a result, low obstacles on the ground—such as dropped delivery boxes or abandoned pallet blocks—or suspended objects just slightly above the ground—such as pallet corners extending from the bottom of shelves—often go completely unnoticed by 2D LiDAR. This makes robots highly susceptible to undercarriage scrapes, cargo collisions, or even impacts against shelf uprights, potentially leading to serious safety incidents.

Collaborative Operations in 3D Warehousing: 3D Safe Interconnection Between AGVs and Overhead Cranes

In modern smart factories, the operational areas of ground logistics (AGVs/AMRs) and aerial logistics (cranes and gantry robots) often overlap significantly. When a crane is lowering a suspended load, if an AGV suddenly enters the area below, a severe “Mount Tai crushing” collision accident can easily occur. Meanwhile, conventional AGVs are equipped only with 2D radar, which can only scan the ground surface and completely “fail to see” objects that are hovering mid-air or lifting devices that are in the process of descending—posing a critical vertical blind-spot hazard.