Booster Robotics
Active
Booster T1
A lightweight, developer-focused humanoid robot built for research, competitions, and rapid prototyp
Price TBA
Humanoid
Robots:
1
Verified (30d):
1
Verified (90d):
1
📋 Evidence & data sources
- Aggregated from each robot's `specs.sensors` field in ui44 data.
🔗 Sample official references
- Booster T1 official page (last verified 2026-03-27)
What Is Intel Realsense D455 RGBD Depth Camera?
Intel Realsense D455 RGBD Depth Camera is a sensor component found in 1 robot tracked in the ui44 Home Robot Database. As a sensor technology, Intel Realsense D455 RGBD Depth Camera plays a specific role in enabling robot perception, interaction, or operation depending on its implementation in each platform.
Sensors are the perceptual backbone of any robot. They convert physical phenomena — light, sound, distance, motion, temperature — into digital signals that the robot's AI can process and act upon. Without sensors, a robot is effectively blind and unable to interact safely with its environment.
In the ui44 database, Intel Realsense D455 RGBD Depth Camera is categorized under Sensor components, which is one of the core technology groupings used to classify robot hardware and software capabilities. It is currently implemented by Booster Robotics, across the Humanoid robot category. For a comprehensive explanation of all component types and their roles in robotics, consult the components glossary.
Why Intel Realsense D455 RGBD Depth Camera Matters in Robotics
The sensor suite is one of the most important differentiators between robots. Robots with richer sensor arrays can navigate more complex environments, avoid obstacles more reliably, recognize objects and people, and perform more nuanced tasks. For buyers, the sensor configuration directly impacts what a robot can actually do in practice — not just on paper.
For robots equipped with Intel Realsense D455 RGBD Depth Camera, this component contributes to the overall capability stack that enables the robot to perform its intended tasks. The 1 robot using Intel Realsense D455 RGBD Depth Camera span the Humanoid category, indicating specialized use across the robotics industry.
How Intel Realsense D455 RGBD Depth Camera Works
Modern robot sensors work by emitting or detecting various forms of energy. Active sensors like LiDAR and ultrasonic emit signals and measure reflections, while passive sensors like cameras and microphones detect ambient energy. The robot's processor fuses data from multiple sensors simultaneously (sensor fusion) to build a coherent understanding of its surroundings.
In the context of Intel Realsense D455 RGBD Depth Camera specifically, the implementation varies by robot platform and manufacturer. Each robot integrates Intel Realsense D455 RGBD Depth Camera differently depending on the overall system architecture, the robot's intended use case, and the specific tasks it needs to perform. The integration of Intel Realsense D455 RGBD Depth Camera with other onboard systems — including other sensors and the main processing unit — determines the real-world performance and reliability of this component.
Intel Realsense D455 RGBD Depth Camera: Detailed Technology Analysis
This section provides an in-depth technical analysis of the specific technologies underlying Intel Realsense D455 RGBD Depth Camera. While the sections above cover general sensor principles, the content below focuses on the particular technology domains relevant to this component based on its implementation characteristics.
Camera & Optical Vision Technology
Camera-based sensors are among the most versatile perception tools available to robots. Unlike single-purpose sensors that measure one physical quantity, cameras capture rich two-dimensional visual information that can be processed by AI algorithms to extract a wide range of insights — from obstacle positions and floor boundaries to object identities, text recognition, and human facial expressions. Modern robot cameras use CMOS image sensors, the same fundamental technology found in smartphones, adapted with specialized lenses and processing pipelines optimized for robotics applications rather than photography.
The optical characteristics of a robot camera significantly affect its utility. Field of view (FOV) determines how much of the environment the camera can see without moving — wide-angle lenses (120°+) provide broad environmental awareness but introduce barrel distortion at the edges, while narrower lenses offer higher angular resolution for object identification at distance. Resolution, measured in megapixels, determines the level of detail captured. For navigation, even a 1-2 megapixel camera may suffice, but for object recognition and facial identification, higher resolutions provide meaningfully better results. Frame rate affects how quickly the robot can respond to environmental changes — 30 fps is standard for navigation, while some safety-critical applications use 60 fps or higher.
Image processing in robotics differs substantially from consumer photography. Robot vision pipelines prioritize low latency over image quality — the robot needs to detect an obstacle within milliseconds, not produce an aesthetically pleasing photo. Hardware-accelerated image processing, often using dedicated ISPs (Image Signal Processors) or neural processing units, enables real-time feature extraction, object detection, and visual odometry (estimating the robot's movement by tracking visual features between frames). The integration of AI models trained specifically for robotics tasks — obstacle classification, floor segmentation, person detection — has transformed camera sensors from simple light-capture devices into intelligent perception systems.
Depth Sensing & 3D Perception
Depth sensors extend robot perception into three dimensions, enabling the detection of objects at varying heights — critical for avoiding furniture legs, detecting items on the floor, and navigating around pets and children. While traditional 2D LiDAR scans at a single horizontal plane, depth sensors provide distance measurements across a two-dimensional field of view, creating a depth map that reveals the 3D structure of the scene.
Several technologies enable depth sensing in robots. Structured light projection casts a known pattern (typically infrared dots or stripes) onto the scene and analyzes the pattern's deformation to calculate distances — the same principle used in early Microsoft Kinect sensors and modern smartphone face scanners. Stereo depth cameras use two horizontally offset cameras (mimicking human binocular vision) and compute depth from the disparity between the two images. Active stereo systems combine stereo cameras with an infrared projector that adds texture to featureless surfaces, improving depth accuracy in environments with plain walls or smooth floors. Time-of-flight depth cameras emit modulated infrared light across their entire field of view and measure the phase shift of the reflected light to determine distance at each pixel simultaneously.
The choice of depth sensing technology involves significant engineering trade-offs. Structured light works well indoors but fails in direct sunlight. Stereo depth cameras have minimum distance limitations and can struggle with textureless surfaces. Time-of-flight sensors offer the best outdoor performance but may have lower resolution than structured light alternatives. For home robots, the operating environment is relatively controlled — consistent indoor lighting, defined room boundaries, and predictable surface types — which allows manufacturers to optimize their depth sensing approach for this specific context rather than requiring the most universal (and expensive) solution.
Implementation Context: Intel Realsense D455 RGBD Depth Camera in the Booster T1
In the ui44 database, Intel Realsense D455 RGBD Depth Camera is currently tracked exclusively in the Booster T1 by Booster Robotics. This humanoid robot integrates Intel Realsense D455 RGBD Depth Camera as part of a total technology stack comprising 10 components: 5 sensors, 4 connectivity modules, and a Intel Core i7-1370P (14 cores); NVIDIA Jetson AGX Orin 32GB (200 TOPS); optional Edge LLM (MiniCPM) AI platform.
A lightweight, developer-focused humanoid robot built for research, competitions, and rapid prototyping. The T1 won the 2025 RoboCup Soccer AdultSize championship and is used by over 50 robotics teams and research labs worldwide. Available in three configurations: Standard (23 DoF), with Grippers (31 DoF), and with Dexterous Hands (41 DoF). Runs on NVIDIA Jetson AGX Orin with 200 TOPS of AI comput…
Visit the full Booster T1 specification page for complete technical details and availability information.
Intel Realsense D455 RGBD Depth Camera works alongside 4 other sensor components in the Booster T1: 9-axis IMU, Circular 6-Mic Array, Speaker, Dual Joint Encoders. This combination of sensor technologies creates the Booster T1's overall sensor capabilities, with each component contributing different aspects of environmental perception.
Intel Realsense D455 RGBD Depth Camera: Technical Deep Dive
Beyond the high-level overview, understanding the technical foundations of sensor technologies like Intel Realsense D455 RGBD Depth Camera helps buyers and researchers evaluate implementations more critically.
Engineering Principles
At its core, every sensor converts a physical quantity into an electrical signal that can be digitized and processed. The transduction mechanism varies by sensor type: optical sensors use photodiodes or CMOS arrays to detect photons, acoustic sensors use piezoelectric elements to detect pressure waves, inertial sensors use micro-electromechanical systems (MEMS) to detect acceleration and rotation, and range sensors use time-of-flight calculations or structured light projection to measure distances. The raw analog output is typically conditioned through amplification, filtering, and analog-to-digital conversion before reaching the robot's main processor.
Performance Characteristics
Sensor performance is characterized by several key metrics. Accuracy measures how close the sensor reading is to the true value. Precision (or repeatability) measures how consistent readings are across multiple measurements of the same quantity. Resolution defines the smallest detectable change. Sampling rate determines how frequently the sensor can take new readings — critical for fast-moving robots that need real-time environmental awareness. Field of view and range define the spatial coverage. Each of these metrics involves engineering trade-offs: higher resolution typically requires slower sampling, wider field of view often reduces angular precision, and longer range may reduce accuracy at close distances.
Technological Evolution
Sensor technology in robotics has evolved dramatically over the past decade. Early home robots relied on simple bump sensors and infrared proximity detectors. Today's platforms incorporate multi-spectral cameras, solid-state LiDAR, millimeter-wave radar, and distributed tactile arrays. Miniaturization has been a key enabler — sensors that once occupied entire circuit boards now fit into packages smaller than a fingernail. Cost reduction through semiconductor manufacturing scale has made previously exotic sensors (like time-of-flight depth cameras and inertial measurement units) standard equipment even on consumer-grade robots. The next frontier is sensor fusion at the hardware level, where multiple sensing modalities are integrated into single chip-scale packages.
Known Limitations
No sensor is perfect in all conditions. Optical sensors struggle in direct sunlight or complete darkness without active illumination. LiDAR can be confused by highly reflective or transparent surfaces like mirrors and glass. Ultrasonic sensors may produce false readings in environments with complex acoustic reflections. Environmental factors like dust, fog, rain, and temperature extremes can degrade sensor performance. Understanding these limitations is important when evaluating robots for specific deployment environments — a robot that works flawlessly in a controlled showroom may encounter challenges in a busy household with pets, children, and varying lighting conditions.
Use Cases & Applications for Intel Realsense D455 RGBD Depth Camera
Sensor technologies in robotics serve a wide range of practical applications. The specific use cases depend on the sensor type, the robot's design, and the target environment. Here are the primary application domains where sensor components play a critical role.
Autonomous Navigation
Sensors enable robots to build maps of their environment, detect obstacles in real time, and plan collision-free paths. This is essential for both indoor robots (navigating furniture and doorways) and outdoor robots (handling terrain variations and weather conditions). The quality and coverage of the sensor array directly determines how reliably a robot can navigate without human intervention.
Object Recognition & Manipulation
Advanced sensors allow robots to identify objects by shape, color, and texture, enabling tasks like picking up items, sorting packages, or recognizing faces. Depth-sensing technologies are particularly important for calculating object distances and sizes, which is necessary for precise manipulation in both home and industrial settings.
Safety & Collision Avoidance
In environments shared with humans, sensors provide the critical safety layer that prevents robots from causing harm. Proximity sensors, bumper sensors, and vision systems work together to detect people and obstacles, triggering immediate stop or avoidance maneuvers. This is a fundamental requirement for any robot operating in homes, hospitals, or public spaces.
Environmental Monitoring
Sensors can measure temperature, humidity, air quality, and other environmental parameters. Robots equipped with these sensors can perform automated monitoring rounds in warehouses, data centers, or homes, alerting users to abnormal conditions like water leaks, temperature spikes, or poor air quality.
Human-Robot Interaction
Microphones, cameras, and touch sensors enable natural interaction between robots and humans. These sensors allow robots to recognize voice commands, detect gestures, respond to touch, and maintain appropriate social distances during conversations or collaborative tasks.
Capabilities Enabled Across Robots With Intel Realsense D455 RGBD Depth Camera
The 1 robot using Intel Realsense D455 RGBD Depth Camera collectively offer 9 distinct capabilities: 23-41 Degrees of Freedom (version-dependent), Bipedal Walking & Running, Self-Recovery (prone to standing), 130 N·m Peak Joint Torque, ROS 2 Compatible, Full SDK for Secondary Development, Mobile App Control (Bluetooth), Firmware OTA Updates, Optional 5G Connectivity. These capabilities represent the practical outcomes of integrating Intel Realsense D455 RGBD Depth Camera alongside other system components. Visit each robot's detail page to see which capabilities are available on specific models.
Robots That Use Intel Realsense D455 RGBD Depth Camera
Intel Realsense D455 RGBD Depth Camera is implemented across 1 robot from 1 manufacturer. Below is a detailed breakdown of each robot, its key specifications, and how Intel Realsense D455 RGBD Depth Camera fits into its overall sensor stack.
Booster T1
by Booster Robotics · Humanoid
A lightweight, developer-focused humanoid robot built for research, competitions, and rapid prototyping. The T1 won the 2025 RoboCup Soccer AdultSize championship and is used by over 50 robotics teams and research labs worldwide. Available in three c…
Active
Sold via inquiry/contact-sales flow on official store (no direct consumer checkout)
Height: 118cmWeight: 30kgBattery: 2 hours walking, 4 hours standing
Released: 2024
Other sensor components on this robot:
Intel Realsense D455 RGBD Depth Camera Across Robot Categories
Intel Realsense D455 RGBD Depth Camera appears in robots spanning 1 category. Understanding which types of robots adopt this technology helps contextualize its role — whether it serves primarily as a consumer convenience, an industrial necessity, or a research enabler.
The following components are most frequently found alongside Intel Realsense D455 RGBD Depth Camera in the same robots. This co-occurrence data reveals which technologies manufacturers commonly pair together, helping you understand typical sensor stacks and integration patterns in the robotics industry.
9-axis IMU
1/1 (100%)
Circular 6-Mic Array
1/1 (100%)
Speaker
1/1 (100%)
Dual Joint Encoders
1/1 (100%)
Wi-Fi 6
1/1 (100%)
Bluetooth 5.2
1/1 (100%)
USB
1/1 (100%)
Ethernet
1/1 (100%)
Intel Core i7-1370P (14 cores); NVIDIA Jetson AGX Orin 32GB (200 TOPS); optional Edge LLM (MiniCPM)
1/1 (100%)
Browse the full components directory or see the components glossary for detailed explanations of each technology.
Intel Realsense D455 RGBD Depth Camera in the Broader Robotics Industry
The robotics sensor market is one of the fastest-growing segments in the broader sensor industry. As robots move from controlled industrial environments into unstructured home and commercial spaces, the demands on sensor technology increase dramatically. Home robots need sensors that are compact, affordable, energy-efficient, and capable of operating reliably in diverse conditions — from bright sunlight to complete darkness, from cluttered living rooms to outdoor gardens. The trend in the industry is toward multi-modal sensing, where robots combine multiple sensor types (vision, depth, tactile, inertial) to build a comprehensive understanding of their environment. Miniaturization and cost reduction continue to make advanced sensors accessible to consumer-grade robots that would have been research-only platforms just a few years ago. Additionally, the integration of AI directly into sensor modules (edge AI) is enabling faster processing and more sophisticated perception without the latency of cloud-based computation.
Within this evolving landscape, Intel Realsense D455 RGBD Depth Camera represents one component in the broader sensor technology stack. Its adoption by 1 robot from 1 manufacturer in the ui44 database provides a data-driven snapshot of real-world industry adoption patterns.
Integration & Ecosystem Compatibility
When evaluating robots with Intel Realsense D455 RGBD Depth Camera, understanding the broader technology ecosystem is essential. Here is what robots using Intel Realsense D455 RGBD Depth Camera support in terms of platform compatibility, voice integration, and AI capabilities.
Platform Compatibility
- ROS 2
- Booster SDK (Python/C++)
- NVIDIA Isaac Sim
- MuJoCo
- Webots
- Mobile App (Bluetooth)
Alternatives to Intel Realsense D455 RGBD Depth Camera
The ui44 database tracks 316 other sensor components alongside Intel Realsense D455 RGBD Depth Camera. Choosing between sensor technologies depends on your specific use case, the robot platform you are evaluating, and how the component integrates with the rest of the robot's technology stack. Below are the most widely adopted alternatives in the same sensor category, ranked by the number of robots using each component.
IMU
25 robots
Force/Torque Sensors
12 robots
Vision System
12 robots
LiDAR
11 robots
Cliff Sensors
9 robots
Microphones
8 robots
Force Sensors
7 robots
Proprioceptive Sensors
6 robots
Browse all Sensor components or use the robot comparison tool to evaluate how different sensor configurations perform across specific robot models.
Buyer Considerations for Intel Realsense D455 RGBD Depth Camera
If Intel Realsense D455 RGBD Depth Camera is an important factor in your robot selection, here are key considerations to guide your decision.
When evaluating sensor specifications, consider: (1) coverage area — does the sensor array provide 360° awareness or only forward-facing detection, (2) range — how far can the robot sense obstacles or objects, (3) resolution — how detailed is the sensor data, and (4) redundancy — does the robot have backup sensors if one fails. Also check whether sensors are user-serviceable or require manufacturer maintenance.
Availability of Robots With Intel Realsense D455 RGBD Depth Camera
Currently, 1 of 1 robots with Intel Realsense D455 RGBD Depth Camera is available for purchase or actively deployed: Booster T1. Check each robot's detail page for the latest availability and purchasing information.
How to Evaluate Intel Realsense D455 RGBD Depth Camera in a Robot
- Integration quality: A component is only as good as its integration. Check how the manufacturer has incorporated Intel Realsense D455 RGBD Depth Camera into the overall robot design and software stack.
- Complementary components: Review what other sensor technologies are paired with Intel Realsense D455 RGBD Depth Camera in each robot — see the related components section above.
- Category fit: Make sure the robot's category matches your use case. Intel Realsense D455 RGBD Depth Camera serves different roles in different robot types.
- Manufacturer track record: Consider the manufacturer's reputation for software updates, support, and component reliability.
Compare Before You Buy
Use the ui44 comparison tool to evaluate robots with Intel Realsense D455 RGBD Depth Camera side by side. Pay attention to the full specification sheet, not just individual components, to ensure the robot meets your overall requirements.
Maintenance & Longevity: Intel Realsense D455 RGBD Depth Camera
Overview
Sensors are among the most maintenance-sensitive components in a robot. Their performance can degrade over time due to physical wear, environmental exposure, and calibration drift. Understanding the maintenance profile of a robot's sensor suite helps set realistic expectations for long-term ownership and operation.
Durability & Reliability
Sensor durability varies significantly by type. Solid-state sensors like IMUs and accelerometers have no moving parts and typically last the lifetime of the robot. Optical sensors like cameras and LiDAR can accumulate dust, scratches, or condensation on their lenses over time. Mechanical sensors such as bump sensors and encoders may experience wear on moving contacts. Environmental sensors for temperature and humidity are generally robust but can be affected by corrosive environments. Overall, sensor failure rates in modern consumer robots are low, but environmental factors like dust accumulation and UV exposure can gradually degrade performance rather than cause sudden failure.
Ongoing Maintenance
Regular sensor maintenance primarily involves keeping optical surfaces clean. Camera lenses, LiDAR windows, and infrared emitters should be wiped with a soft, lint-free cloth to remove dust and fingerprints. Many modern robots perform automatic sensor self-diagnostics and will alert users when calibration has drifted beyond acceptable limits. Some robots support user-initiated recalibration routines for specific sensors. For robots used in dusty or pet-heavy environments, more frequent cleaning of sensor surfaces may be necessary. Manufacturer documentation typically includes sensor care instructions specific to the robot's sensor configuration.
Future-Proofing Considerations
When evaluating sensor technology for long-term value, consider the manufacturer's track record for software updates that improve sensor utilization. A robot with good sensors and ongoing software development can actually improve its performance over time as algorithms are refined. However, sensor hardware itself cannot be upgraded post-purchase on most consumer robots, making the initial sensor specification an important long-term consideration. Robots with modular sensor designs that allow component replacement offer better long-term maintainability, though this is currently more common in commercial and research platforms than consumer products.
For the 1 robot in the ui44 database using Intel Realsense D455 RGBD Depth Camera, we recommend checking the individual robot pages for manufacturer-specific maintenance guidance and support documentation. Each manufacturer has different support policies, update frequencies, and warranty terms that affect the long-term ownership experience of their sensor technologies.
Troubleshooting & Common Issues: Intel Realsense D455 RGBD Depth Camera
Sensor-related issues are among the most common problems home robot owners encounter. Many sensor issues can be resolved with simple maintenance or environmental adjustments, while others may indicate hardware problems requiring manufacturer support. Understanding common failure modes helps you diagnose and resolve issues quickly, minimizing robot downtime.
Robot bumps into obstacles it should detect
Likely causes: Dirty or obstructed sensor windows are the most frequent cause. Dust, pet hair, fingerprints, or cleaning solution residue on LiDAR, camera, or infrared sensor surfaces significantly reduce detection accuracy. Highly reflective surfaces like mirrors, glass doors, and glossy furniture can also confuse optical and laser-based sensors by creating phantom readings or absorbing signals entirely.
Resolution: Clean all sensor windows and lenses with a soft, dry microfiber cloth. Avoid chemical cleaners unless the manufacturer specifically recommends them. If cleaning does not resolve the issue, check for recent firmware updates that may address sensor calibration. For persistent problems with specific surfaces, consider applying anti-reflective film to mirrors or glass surfaces in the robot's operating area.
Robot map becomes inaccurate or corrupted over time
Likely causes: Sensor drift and calibration degradation can cause mapping errors. Significant furniture rearrangement, new obstacles, or changed room layouts may confuse the mapping algorithm. In some cases, electromagnetic interference from nearby electronics can affect sensor readings used for localization.
Resolution: Delete and rebuild the map from scratch using the manufacturer's app. Ensure the robot's firmware is up to date, as mapping improvements are frequently included in updates. If the problem recurs, run the robot during periods of minimal household activity to get the cleanest initial map.
Cliff or drop sensors trigger on flat surfaces
Likely causes: Dark-colored flooring, transitions between floor materials, and thick carpet edges can trigger infrared cliff sensors. Direct sunlight hitting the floor near the robot can also interfere with infrared detection by saturating the sensor with ambient infrared light.
Resolution: Clean the cliff sensors on the underside of the robot. If the issue occurs at specific locations consistently, check whether the floor has very dark patches, strong color transitions, or high-gloss finishes that might confuse the sensors. Some manufacturers allow cliff sensor sensitivity adjustment through the companion app.
When to contact the manufacturer
Contact the manufacturer if sensor issues persist after cleaning and firmware updates, if you notice physical damage to any sensor housing, or if the robot reports sensor errors in its diagnostic log. Sensor calibration that cannot be corrected through standard procedures may indicate hardware degradation requiring professional service or component replacement.
For model-specific troubleshooting, visit the individual robot pages for the 1 robot using Intel Realsense D455 RGBD Depth Camera. Each manufacturer provides model-specific support resources and diagnostic tools for their sensor implementations.
Frequently Asked Questions About Intel Realsense D455 RGBD Depth Camera
What is Intel Realsense D455 RGBD Depth Camera in robotics?
Intel Realsense D455 RGBD Depth Camera is a sensor component used in 1 robot tracked in the ui44 Home Robot Database. It falls under the Sensor category, which encompasses technologies that enable robots to perceive and measure their environment. Visit the components glossary for a complete guide to robot component types.
Which robots use Intel Realsense D455 RGBD Depth Camera?
Intel Realsense D455 RGBD Depth Camera is used in 1 robot from 1 manufacturer: Booster T1 (Booster Robotics). See the full list in the robots section above.
What types of robots typically use Intel Realsense D455 RGBD Depth Camera?
Intel Realsense D455 RGBD Depth Camera is found across 1 robot category: Humanoid. Its presence in the Humanoid category indicates specialized use within that domain.
How much do robots with Intel Realsense D455 RGBD Depth Camera cost?
Currently, none of the robots with Intel Realsense D455 RGBD Depth Camera list public pricing. This is typical for enterprise, research, or development-stage robots. Contact the manufacturers directly for pricing information.
Can I buy a robot with Intel Realsense D455 RGBD Depth Camera today?
Yes — 1 robot with Intel Realsense D455 RGBD Depth Camera is currently available or actively deployed: Booster T1. Visit each robot's page for purchasing details.
What other components are commonly used with Intel Realsense D455 RGBD Depth Camera?
The most common components paired with Intel Realsense D455 RGBD Depth Camera include: 9-axis IMU (1 of 1 robots), Circular 6-Mic Array (1 of 1 robots), Speaker (1 of 1 robots), Dual Joint Encoders (1 of 1 robots), Wi-Fi 6 (1 of 1 robots). See the full co-occurrence analysis above.
What type of component is Intel Realsense D455 RGBD Depth Camera?
Intel Realsense D455 RGBD Depth Camera is classified as a Sensor in the ui44 database. Sensors are the technologies that allow robots to perceive their environment — detecting obstacles, measuring distances, recognizing objects, and monitoring conditions. Browse all Sensor components in the database.
Does Intel Realsense D455 RGBD Depth Camera require maintenance?
As a sensor component, Intel Realsense D455 RGBD Depth Camera may require periodic maintenance depending on the specific implementation. Optical sensor surfaces should be kept clean and free of dust or debris. Solid-state sensors generally require no physical maintenance. Most robots perform automatic self-diagnostics on their sensors and will alert you if calibration drift or degradation is detected. See the maintenance and longevity section for detailed guidance.
What are alternatives to Intel Realsense D455 RGBD Depth Camera?
The ui44 database tracks 5 different sensor components across all robots. Alternatives to Intel Realsense D455 RGBD Depth Camera depend on your specific use case and the robot platform you are considering. The related components section above shows which other sensor technologies are frequently paired with Intel Realsense D455 RGBD Depth Camera, and the Sensor components directory provides a complete listing of all tracked sensor technologies. Use the robot comparison tool to evaluate how different sensor configurations perform in practice.
How current is the Intel Realsense D455 RGBD Depth Camera data on ui44?
All component data on ui44 is derived from verified robot specifications. The most recent verification for a robot using Intel Realsense D455 RGBD Depth Camera was on 2026-03-27. Robot data is periodically re-verified against manufacturer sources to ensure accuracy. Each robot page shows its individual "last verified" date.
Data Integrity
Intel Realsense D455 RGBD Depth Camera data on ui44 is derived from verified robot specifications, official manufacturer documentation, and press releases. Most recent robot verification: 2026-03-27. Component associations are automatically extracted from each robot's spec sheet and normalized for consistency across the database.
Source: ui44 Home Robot Database · 1 robot tracked · Browse all components · Components glossary · Full robot directory
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