LiDAR 3D Scanner for Robotics, Drones & Industrial Vision: How to Choose the Right Module

Searching for a lidar 3d scanner can get messy fast. One search result points you to a phone app for scanning a living room. The next one shows a tripod scanner for construction survey work. Another walks you through a Raspberry Pi build using a rotating 2D LiDAR and a hobby servo. Here’s the deal: those may all involve LiDAR or 3D data, but they are not built for the same job.

If you are working on robotics perception, UAV obstacle detection, AMR navigation, industrial inspection, smart security, or embedded 3D vision, the real question is not just “Can this thing make a 3D model?” The better question is: can this LiDAR module deliver stable depth data, usable point clouds, predictable timing, and clean integration in the environment where the machine actually has to work?

This guide looks at how to evaluate a lidar 3d scanner from an engineering and procurement point of view. We will cover ranging method, accuracy, field of view, resolution, frame rate, interfaces, SDK support, point cloud output, power consumption, outdoor performance, mounting constraints, and the common mistakes that burn project time.

You will also see where a compact solid-state dToF module such as the DTOF Solid State LiDAR HM-LD1 fits into real-world robotics, UAV, inspection, smart monitoring, and industrial vision applications. Look, no sensor is magic. The right one depends on the job. But if you know what to check before buying, you can avoid most of the expensive surprises.

What Is a LiDAR 3D Scanner?

A LiDAR 3D scanner is a sensing device that measures distance using emitted light and returned optical signals. In plain shop-floor language, it sends light into the environment, waits for the reflected signal to come back, and calculates distance based on the behavior of that return. Depending on the sensor and software, the output may be a depth map, point cloud, range image, occupancy grid, or a processed 3D model.

That last part matters. Not every product labeled as a lidar 3d scanner is built for the same outcome. A consumer app may focus on making a room look nice in AR. A terrestrial laser scanner may focus on dense survey-grade point clouds. An automotive LiDAR may focus on long-range perception. A robotics LiDAR module usually has a different job: provide real-time, machine-readable depth data that software can use for navigation, obstacle avoidance, inspection, measurement, or safety-zone logic.

Consumer 3D Scanning vs Industrial 3D Perception

Consumer scanning is mostly about visual reconstruction. The user walks around a space or object, and the app tries to build a mesh or textured model. That is useful for interior planning, AR previews, rough documentation, and quick visualization. But it is usually not designed for embedded control, fixed mounting, predictable latency, industrial communication, or continuous operation in bright sunlight, vibration, dust, or temperature swings.

Industrial 3D perception is a different animal. Engineers care about repeatable range measurements, known frame rates, stable outputs, supported interfaces, mounting geometry, field of view, SDK documentation, and behavior under real operating conditions. In the shop, a robot does not need a pretty mesh. It needs to know whether a pallet fork is in the way, whether a person entered a restricted zone, whether a drone is too close to a wall, or whether an inspection platform is holding the right standoff distance.

Why “Scanner” Means Different Things in Robotics

In robotics, the word “scanner” often means a sensor that continuously measures the surrounding environment, not necessarily a device that produces a CAD-ready 3D model. A 40 × 30 depth module, for example, is not trying to compete with a high-density survey scanner. Still, it can be extremely useful for obstacle detection, presence sensing, zone monitoring, distance measurement, object localization support, and robotic vision development.

For UAV-specific selection issues like weight, power budget, mounting direction, vibration, sunlight, and outdoor range, see our LiDAR drone buying guide. Drones are their own problem set. Payload, airflow, flight speed, and sunlight can change what “good enough” means very quickly.

How LiDAR 3D Scanning Works

LiDAR 3D scanning starts with optical ranging. The sensor emits light into a scene and measures the returned signal after it reflects from a target. By doing that across multiple directions in its field of view, the system builds a spatial representation of what is in front of it. Depending on the architecture, that can happen with a scanning beam, a flash-style array, a solid-state depth sensor, or a mechanically rotating assembly.

Time-of-Flight Ranging

Time-of-flight ranging is one of the core principles behind LiDAR depth sensing. The sensor emits light, the light travels to a surface, reflects, and returns to the receiver. Since the speed of light is known, the electronics can calculate distance from the measured time delay. The distance calculation accounts for the round trip, so the measured travel is divided by two.

That sounds simple on paper. In the real world, the sensor has to deal with weak returns, dark surfaces, angled targets, bright sunlight, optical noise, signal filtering, timing precision, and host communication. That is why the receiver, timing electronics, signal processing, firmware, and SDK can matter just as much as the advertised range number.

Direct Time-of-Flight vs Indirect Time-of-Flight

Direct time-of-flight, often called dToF, measures the actual return time of emitted photons. It is well suited for direct ranging, outdoor adaptability, and timing-based distance measurement. Indirect time-of-flight, or iToF, typically measures the phase shift of modulated light and is common in many short-range depth cameras. Structured light projects a known pattern and reads the deformation of that pattern. Stereo vision estimates depth by comparing two camera views.

The HM-LD1 is positioned as a solid-state SPAD dToF LiDAR module. In practical terms, it provides real-time depth images and 3D point cloud data for embedded sensing tasks without relying on a large mechanical rotating scanner or a consumer-style reconstruction workflow.

What Is a Point Cloud?

A point cloud is a collection of 3D coordinate points that represent measured surfaces or objects in space. Each point usually carries X, Y, and Z position information, and may also include intensity or confidence values depending on the sensor. Point clouds are widely used in robotics, autonomous navigation, inspection, segmentation, mapping assistance, object detection, and volume estimation.

For a robot, a point cloud can help decide whether a path is blocked. For a drone, it can support terrain following or obstacle detection. For smart security, it can help detect whether a person or object entered a defined zone. For industrial vision, it can assist with approximate package volume, bin fill level, position checks, or process monitoring.

What Is a Depth Map?

A depth map represents distance values across a two-dimensional measurement grid. Each pixel or cell corresponds to a direction in the field of view and stores a measured distance. A 40 × 30 depth resolution gives 1,200 distance measurement points per frame. That may be easier to process than a dense point cloud when the task is distance thresholding, occupied-zone detection, obstacle monitoring, or height measurement.

Why Resolution Is Not the Only Metric

Buyers often get hung up on resolution. I get it. More points sounds better. But in the field, resolution alone does not determine whether a lidar 3d scanner is right for the job. A high-resolution sensor with poor outdoor behavior, high power draw, limited SDK support, or awkward mounting can slow down a project fast. A lower-resolution embedded LiDAR module with stable range data, low power consumption, practical FOV, and clean interfaces may be the better choice for drones, AMRs, inspection robots, and industrial monitoring systems.

Types of LiDAR 3D Scanners Compared

Before choosing a LiDAR scanner module, it helps to sort the major categories. The right product depends on whether you need visual scanning, survey mapping, DIY experimentation, full-surround perception, or compact embedded depth sensing.

Handheld and Mobile Phone LiDAR Scanners

Phone and tablet LiDAR scanners are convenient for quick room capture, AR visualization, rough object scans, and consumer documentation. They are easy to use and usually need very little technical setup. But they are not industrial robotics sensors. Their outputs are app-dependent, their workflow is manual, and their data pipeline is not meant for real-time machine control.

For automation, phones bring mechanical and operational headaches. They are not rugged industrial sensors, they are not designed to be permanently mounted into machines, and they usually do not provide the interface flexibility required for embedded control. A phone LiDAR scan can help with planning or visualization, but it should not be confused with a dedicated LiDAR depth module.

Terrestrial Laser Scanners

Terrestrial laser scanners are used in construction, surveying, plant documentation, infrastructure inspection, building modeling, and large-asset reverse engineering. They can produce dense point clouds with high spatial detail, often from a tripod or fixed station. They are powerful tools, but they are usually expensive, heavy, and optimized for scanning workflows rather than embedded machine perception.

If the job is survey-grade building capture, a terrestrial scanner may be the correct tool. If the job is putting a small depth sensor on a robot, drone, smart camera, or inspection platform, a compact solid-state dToF LiDAR module is usually more practical.

Rotating 2D LiDAR DIY Scanners

A common DIY approach is to take a 2D LiDAR and rotate or tilt it with a servo, stepper motor, or mechanical platform. By collecting multiple 2D slices at different angles, the system can stitch those measurements into a 3D point cloud. It is a solid educational project for learning ROS, coordinate transforms, calibration, timing, and point cloud registration.

But production systems are less forgiving. DIY rotating scanners introduce mechanical wear, vibration, alignment drift, synchronization problems, frame stitching issues, and motion distortion. In the lab, that may be part of the fun. In a commercial robot, drone, AMR, or inspection system, the integration burden can wipe out the savings from cheap hardware.

Mechanical Spinning 3D LiDAR

Mechanical spinning 3D LiDAR systems are common in autonomous vehicle research, outdoor mapping, and applications that need broad spatial coverage or 360-degree perception. They can provide high point density and wide-area awareness. The tradeoff is usually larger size, higher price, higher power consumption, and moving components.

They can be excellent for high-end mobile mapping and autonomous platforms. But for compact UAVs, small robots, fixed monitoring devices, smart cameras, or focused obstacle detection, a mechanical spinning LiDAR may be more sensor than the application needs.

Solid-State dToF LiDAR Modules

Solid-state dToF LiDAR modules are designed for compact embedded sensing. They are well suited for robots, drones, AMRs, AGVs, smart security devices, industrial cameras, inspection platforms, and development systems. Because they do not require a large rotating assembly, they can be smaller, lighter, easier to mount, and less mechanically fragile.

The practical benefit is integration efficiency. A solid-state dToF module can output depth maps and point cloud data directly to a host system, letting the engineering team spend time on perception logic, safety rules, navigation behavior, and inspection workflow instead of building a mechanical scanner from scratch.

For broader industry context, companies such as Cepton Technologies and Innoviz Technologies show how LiDAR continues to evolve across automotive and industrial perception markets.

Key Specs to Compare Before Buying a LiDAR 3D Scanner

For industrial buyers and R&D teams, the right lidar 3d scanner should be selected by application requirements, not by a single headline specification. Range, accuracy, field of view, resolution, frame rate, interface, SDK support, power, size, weight, temperature range, and outdoor robustness all matter.

Ranging Capability

Ranging capability should be checked separately for indoor and outdoor conditions. Indoor or nighttime environments usually have lower optical background noise, so the sensor may achieve better effective range. Outdoor sunlight increases background noise at the receiver and can reduce usable range. Target reflectivity, color, angle, distance, and weather also affect performance.

Do not assume that an indoor range spec applies outdoors. A drone inspection platform, outdoor security device, or field robot must be tested in realistic illumination. For example, the HM-LD1 product information specifies indoor ranging of 0.5–25 m and outdoor ranging of 0.2–8 m, giving engineers a clearer planning window.

Ranging Accuracy

Accuracy tells you how closely the measured distance matches the true distance. In practice, accuracy depends on target reflectivity, ambient light, calibration, distance, angle, signal quality, mounting stability, and filtering. The HM-LD1 specifies ranging accuracy of ±3 cm, which is useful for many obstacle detection, distance monitoring, presence detection, and robotic vision tasks.

Match accuracy to the real job. A drone altitude support system, AMR obstacle layer, or zone monitoring device may not need metrology-grade precision. A precision reverse-engineering scanner or detailed small-part measurement station probably does.

Field of View

Field of view, or FOV, defines how much of the scene the sensor sees. A wider FOV gives broader coverage, but the available measurement points are spread across that space. For robots and drones, FOV affects how much space can be monitored in front of, below, or around the platform.

The HM-LD1 provides a 60° horizontal by 45° vertical FOV. That can support forward-looking obstacle detection, downward-looking height sensing, or fixed-zone monitoring depending on mounting position and software logic.

Resolution

Resolution describes the number of measurement cells or depth points per frame. A 40 × 30 resolution means 1,200 measurement points per frame. That is not meant for ultra-dense 3D reconstruction of tiny mechanical parts. It is more appropriate for real-time embedded perception, obstacle detection, presence sensing, distance monitoring, zone intrusion, and navigation assistance.

Frame Rate

Frame rate determines how often the sensor updates its depth data. A 10 fps LiDAR depth module provides ten depth frames per second. For slow-to-medium speed robots, inspection systems, presence detection, and monitoring applications, that can be practical when paired with proper filtering and control logic. For faster platforms, engineers must evaluate speed, stopping distance, latency, and safety margins.

Interface Options

Interface selection directly affects integration cost. UART is useful for embedded controllers and simpler command/data communication. UDP is practical for robotics computers, industrial PCs, and network-style transmission. UVC can simplify testing and development by letting the sensor behave like a video or depth stream on compatible host systems.

A module that supports UART, UDP, and UVC gives engineers flexibility during prototyping and deployment. The HM-LD1 supports all three, making it easier to work with PCs, Raspberry Pi-class platforms, embedded Linux systems, flight controllers, and industrial computers depending on system architecture.

SDK and Platform Support

SDK support is easy to underestimate until the schedule is already slipping. A capable sensor can still slow a project if developers cannot access data, configure parameters, compile examples, or connect the module to the target operating system. Support for x86 Windows, x86 Linux, and ARM Linux is valuable because teams often prototype on a PC and later migrate to an embedded computer or industrial controller.

Development teams may use C++, Python, ROS, OpenCV, custom middleware, or vendor APIs. Confirming SDK support before procurement can save weeks of debugging.

Power Consumption

Power consumption matters on drones, battery-powered robots, remote monitoring systems, and compact embedded devices. Lower power reduces thermal load and helps preserve runtime. The HM-LD1 specifies 1.2 W power consumption, which is attractive for UAVs and mobile platforms where every watt counts.

Size and Weight

Mechanical integration is just as important as electrical integration. A sensor that is too large can block other components, interfere with airflow, increase vibration sensitivity, or complicate mounting. Weight is especially important for drones because payload directly affects flight time, stability, and available mission equipment.

The HM-LD1 is specified at 43.5 mm × 30 mm × 26.5 mm and 28 g, making it suitable for compact robots, drones, industrial cameras, and embedded perception devices with tight space limits.

Operating Temperature

Operating temperature tells you whether the sensor can survive and perform in the target environment. Warehouses, outdoor robots, inspection systems, and unmanned platforms may face cold starts, hot enclosures, direct sun, and changing industrial conditions. The HM-LD1 specifies an operating temperature of -20 ℃ to 60 ℃, supporting a broad range of robotics and industrial use cases.

Robotics, Drone & Industrial Use Cases

A LiDAR 3D scanner module can play many roles in automation and machine perception. The trick is matching the output and geometry to the job instead of treating every 3D scanning application as the same problem.

AMR and AGV Obstacle Avoidance

Autonomous mobile robots and automated guided vehicles need reliable awareness of their surroundings. Depth maps and point clouds can support obstacle detection, collision avoidance, height awareness, pallet detection support, and local safety-zone monitoring. A forward-facing LiDAR depth sensor can help detect objects that simple proximity sensors may miss.

Mobile Robot Navigation and SLAM Assistance

LiDAR depth data can support navigation stacks, local obstacle layers, object detection, and mapping assistance. A low-resolution module should not be oversold as a complete SLAM replacement in every environment. In many machines, it works best as part of a sensor suite that may include wheel odometry, IMU, 2D LiDAR, RGB camera, GNSS, or other perception devices.

Drone Altitude Hold and Terrain Following

Drones benefit from lightweight, low-power sensing. A downward-facing LiDAR module can support altitude measurement and terrain following. A forward-facing configuration can help detect obstacles in the flight path. Weight, field of view, update rate, vibration, sunlight, and target reflectivity all need real testing.

For more UAV selection details, read our LiDAR drone buying guide.

Industrial Inspection

Inspection robots and UAVs often need to measure distance to structures that are difficult, hazardous, or inefficient for people to approach. Examples include bridges, expressways, dams, industrial equipment, confined spaces, restricted-access areas, and infrastructure surfaces. The HM-LD1 product content highlights that it can measure at distances up to 8 m outdoors on a clear summer day assuming 80,000 lux, making it relevant for certain field inspection and robotic patrol scenarios.

Smart Security and Zone Intrusion Monitoring

Depth sensing can help security systems detect presence, distance, and zone intrusion without relying only on visible-light image recognition. A depth-based system can define monitored regions, detect objects entering a zone, and estimate distance. This can be useful for industrial safety boundaries, access control, perimeter devices, machine guarding support, and privacy-sensitive monitoring where full visual identification is not always needed.

Autofocus, Object Recognition and Volume Measurement

Depth information can assist camera autofocus, object recognition, approximate package volume estimation, user presence detection, and smart equipment interaction. A LiDAR depth sensor can provide distance information that complements RGB cameras or other machine vision sensors. Explore additional robot and UAV sensing scenarios on our applications page.

Recommended Solid-State dToF LiDAR Module: DTOF Solid State LiDAR HM-LD1

The DTOF Solid State LiDAR HM-LD1 is a compact SPAD dToF LiDAR module designed for real-time depth sensing, point cloud output, obstacle avoidance, distance detection, autonomous navigation, smart inspection, and embedded robotic vision development. For engineers searching for a lidar 3d scanner module rather than a consumer mesh scanner, HM-LD1 offers a practical mix of compact size, low weight, low power consumption, and flexible interfaces.

HM-LD1 outputs real-time depth images and 3D point cloud data. It supports 0.5–25 m ranging indoors and 0.2–8 m outdoors, with ±3 cm ranging accuracy. Its UART, UDP, and UVC interfaces support multiple development and deployment architectures, while SDK support for x86 Windows, x86 Linux, and ARM Linux helps teams move from prototyping to embedded integration.

View DTOF Solid State LiDAR HM-LD1 or download the DTOF SSL HM-LD1 product brochure.

View Product Details & Pricing ➔

Why HM-LD1 Fits Embedded LiDAR 3D Scanner Applications

HM-LD1 is a strong fit when the project needs compact embedded depth perception rather than high-density survey scanning. Its 28 g weight is suitable for drones and small robots where payload is tightly controlled. Its 1.2 W power consumption supports battery-powered platforms and compact devices where power and heat must be managed carefully.

The 60° × 45° FOV gives engineers a practical sensing window for forward-looking obstacle detection, downward-looking altitude sensing, or fixed-area monitoring. At 10 fps, the module provides real-time depth updates for monitoring, navigation support, and robotic vision tasks when system speed and control logic are designed properly. UART, UDP, and UVC interfaces allow teams to prototype on PCs and then integrate with embedded controllers or Linux-based robotics computers.

Where HM-LD1 Is Not the Right Fit

Trustworthy sensor selection also means knowing when a product is not the best fit. HM-LD1 is not intended to replace survey-grade terrestrial laser scanners for dense building mapping. It is not a consumer mesh-generation app. It is not the right choice if the application requires ultra-dense 3D reconstruction of tiny mechanical parts with metrology-grade detail.

Its strongest role is as an embedded LiDAR depth sensor for robots, drones, inspection platforms, industrial cameras, smart security systems, and R&D projects that need real-time depth maps and point cloud data in a compact module.

How to Integrate a LiDAR 3D Scanner Module into a Robot, Drone or Vision System

Successful LiDAR integration is not only about buying a sensor. It requires defining the measurement task, selecting the mounting location, choosing the interface, processing the depth data, and validating performance in the real environment. A structured workflow reduces risk and helps avoid late-stage redesign.

Step 1 — Define the Measurement Task

Start with the job. Do you need obstacle detection, mapping assistance, object presence, altitude hold, terrain following, zone intrusion detection, distance control, or volume measurement? What is the minimum range? What is the maximum range? What accuracy is required? Is the environment indoor, outdoor, or mixed? Are the targets dark, reflective, transparent, angled, irregular, dusty, wet, or moving?

Step 2 — Select Mounting Position and FOV

Mounting position determines what the sensor can actually see. A forward-facing module can support obstacle detection. A downward-facing module can support altitude hold or terrain following. An angled module can monitor bins, conveyor zones, shelves, doorways, docking stations, or safety regions. Multiple sensors may be needed when wider coverage is required.

Step 3 — Choose Interface and Host Platform

Choose the interface based on the host processor and data architecture. UART is suitable for embedded control and simpler communication. UDP is useful for robotics computers or network-style data handling. UVC can simplify early testing by allowing depth stream behavior on compatible PC or Linux platforms. HM-LD1’s support for UART, UDP, and UVC gives engineers several integration paths.

Step 4 — Process Depth Map or Point Cloud

After data acquisition, the software has to turn measurements into decisions. Processing may include thresholding by distance, segmenting objects, converting depth maps into 3D coordinates, filtering noise, detecting occupied zones, or fusing LiDAR data with IMU, camera, wheel odometry, GNSS, or other sensors. ROS nodes may be useful during development, but production systems often need application-specific filtering and safety logic.

Step 5 — Validate in the Real Environment

Validation should include sunlight, dust, vibration, target reflectivity, motion speed, temperature, mounting angle, cable stability, electromagnetic noise, and latency. In the shop, a sensor may look perfect on a bench and behave differently on a drone, mobile robot, outdoor inspection platform, or industrial machine. For market and technology updates, see our LiDAR news today resource.

LiDAR 3D Scanner Selection Checklist for Industrial Buyers

Use this checklist when evaluating a lidar 3d scanner for robotics, drones, AMR/AGV, industrial vision, or embedded perception:

• ✅ Range: Confirm indoor and outdoor range separately.
• ✅ Accuracy: Check stated accuracy and the real-world test conditions behind it.
• ✅ FOV: Match horizontal and vertical coverage to the actual sensing zone.
• ✅ Resolution: Decide whether you need dense modeling or real-time obstacle perception.
• ✅ Frame rate: Verify whether the sensor updates fast enough for platform speed.
• ✅ Interface: Select UART, UDP, UVC, Ethernet, USB, or another required output.
• ✅ SDK support: Confirm Windows, Linux, ARM Linux, ROS, OpenCV, or custom API compatibility.
• ✅ Power: Check consumption for battery-powered robots or UAVs.
• ✅ Size and weight: Evaluate mounting, payload limits, and enclosure space.
• ✅ Operating temperature: Match the module to field conditions.
• ✅ Outdoor performance: Review sunlight tolerance and target reflectivity assumptions.
• ✅ Data output: Confirm depth maps, point clouds, raw ranging data, or processed visualization.
• ✅ Technical support: Choose suppliers that support integration, calibration, and application testing.

Common Mistakes When Choosing a LiDAR 3D Scanner

Many failed sensor selections happen because the buyer compares the wrong criteria. A low price, impressive maximum range, or high resolution may look attractive, but deployment success depends on whether the scanner fits the environment, interface, software workflow, power budget, and mechanical constraints.

Mistake 1 — Buying Based Only on Maximum Range

Maximum range is often measured under specific lighting, reflectivity, and target conditions. Real-world performance depends on ambient sunlight, target angle, surface material, color, weather, optical noise, and filtering. Always ask whether the stated range applies indoors, outdoors, at night, or under bright daylight.

Mistake 2 — Confusing 3D Modeling with Real-Time Depth Sensing

A scanner for producing visual meshes is not automatically the best sensor for robotics perception. Robots need reliable data streams, low latency, predictable outputs, and integration-friendly interfaces. A consumer 3D scanning workflow may not support autonomous decision-making.

Mistake 3 — Ignoring Outdoor Sunlight Performance

A depth sensor that works indoors may perform poorly in sunlight. Outdoor platforms, drones, inspection robots, and security systems should be tested in realistic illumination. Outdoor range specifications should always be reviewed separately from indoor specifications.

Mistake 4 — Underestimating Integration Cost

DIY or low-cost sensors may appear cheaper at first, but mechanical design, calibration, synchronization, filtering, ROS processing, mounting, and debugging can consume significant engineering time. An SDK-ready LiDAR scanner module may reduce total project cost even if the unit price is higher.

Mistake 5 — Choosing Too Much Sensor

A high-end mechanical 3D LiDAR may be unnecessary for simple zone monitoring, presence detection, altitude support, or near-field obstacle sensing. Over-specifying the sensor can increase cost, power consumption, payload, and integration complexity.

Mistake 6 — Choosing Too Little Sensor

Ultrasonic or basic infrared proximity sensors may be insufficient when spatial awareness, object localization, multi-zone monitoring, or point cloud data is required. If the robot or industrial system needs a usable representation of the scene, a LiDAR depth sensor may be more appropriate.

FAQ: LiDAR 3D Scanner Selection, DIY Options and Industrial Integration

Final Recommendation: Choose the LiDAR 3D Scanner by Application, Not by Hype

The best lidar 3d scanner is the one that fits the actual job. If you need quick room scans, AR visualization, or rough consumer capture, a phone or tablet LiDAR app may be enough. If you need survey-grade building documentation, a terrestrial laser scanner is usually the right category. If you need long-range, wide-area, high-density perception for advanced autonomous vehicles, a mechanical spinning 3D LiDAR may be appropriate.

If your goal is compact embedded perception for robots, drones, industrial vision, inspection, smart security, or development platforms, a solid-state dToF LiDAR module is often the practical choice. The DTOF Solid State LiDAR HM-LD1 provides indoor 0.5–25 m ranging, outdoor 0.2–8 m ranging, ±3 cm accuracy, 60° × 45° FOV, 40 × 30 resolution, 10 fps output, UART/UDP/UVC interfaces, 1.2 W power consumption, 28 g weight, and SDK support for x86 Windows, x86 Linux, and ARM Linux.

If you are developing a robot, drone, inspection platform, or industrial vision system, review the DTOF Solid State LiDAR HM-LD1 or download the HM-LD1 product brochure to evaluate whether its range, accuracy, FOV, interfaces, and SDK support match your project.