Estás trabajando en un proyecto de robótica. ¿Qué sistemas de visión le darán los resultados más precisos?

In my experience, LIDAR (Light Detection and Ranging) systems offer exceptional accuracy for spatial mapping and obstacle detection in robotics, as demonstrated by autonomous vehicles like those developed by Waymo and Tesla's use of advanced computer vision technologies paired with neural networks for real-time object detection and navigation.

In robot world, seeing is like believing, but real magic is in picking how they see things. Whether it's stereo or lidar, infrared or neural networks, right vision turns robots from just watching to truly understanding our tech-filled adventure. If you're doing robot project, think about using stereo vision. This method uses two cameras to take pictures of same thing from a bit different angles. By comparing these pictures, you can figure out how far away things are. Stereo vision is handy for robot projects that want to understand 3D shape of area, like avoiding obstacles or making maps. Stereo vision needs careful setup between cameras & has some challenges, like being affected by shadows, certain surfaces, & needing precise calibration.

Accuracy in selecting vision systems for a robotics project, including stereo vision, structured light, ToF cameras, and LiDAR, is project-dependent. High-resolution LiDAR offers long-range but at a higher cost, while solid-state flash LiDAR provides longer range than ToF but with less resolution and depth accuracy. The choice should balance range, accuracy, and cost, tailored to the project's specific requirements.

Stereo vision, utilizing two cameras capturing slightly different viewpoints of a scene, emerges as a prime choice for achieving precision in robotics projects. This technique enables depth and distance estimation through meticulous image comparison, proving invaluable for understanding 3D environments in applications like obstacle avoidance, mapping, and grasping. Despite its efficacy, implementation requires meticulous calibration, synchronization, and camera alignment. Sensitivity to factors like occlusions and lighting conditions necessitates careful consideration when integrating this vision system, emphasizing the need for a nuanced approach.

Robotic vision relies on stereo vision, which uses two cameras to see in 3D like our eyes. Robots can navigate, manipulate, and recognise things by calculating distance and building a 3D map of their surroundings. Stereo vision is passive, provides abundant information, and is cheaper than LiDAR. Its computational complexity, illumination sensitivity, and range are drawbacks. Stereo vision is still useful in robotic vision, with new algorithms improving accuracy and resilience.

Monocular vision, relying on a single camera, is a valuable vision system with versatile applications. It excels in tasks where depth perception is not the primary concern, such as object recognition, image classification, and visual inspection. For instance, in a manufacturing setup, monocular vision can be employed for quality control by identifying defects on products. It is also valuable in robotics navigation, allowing robots to follow predetermined paths or detect and avoid obstacles. While monocular vision may lack depth information compared to stereo vision, its simplicity, cost-effectiveness, and suitability for specific applications make it a preferred choice in various scenarios.

Monocular vision is a single camera system used in robotics for observing the environment. It lacks depth perception like stereo vision, but offers valuable capabilities and is cost-effective. It uses techniques like depth estimation algorithms and learning from paired data to associate image features with depth. Applications include object detection, obstacle avoidance, visual odometry, and line following. Advantages include cost-effectiveness, simplicity, and compactness. Disadvantages include limited depth perception, sensitivity to lighting and environment, and computational demands. Despite these limitations, monocular vision is a versatile and affordable tool for robots, excelling in tasks like object recognition and navigation.

Monocular vision is much less costly in terms of price, size, space and market availability than other solutions. It is not necessarily simpler, however, as it will require subsequent image processing. If possible, I recommend starting with monocular vision first. Other vision solutions should only be considered if monocular vision cannot be applied in your case, or if its results are insufficient (accuracy, lighting conditions, image depth, reflections, etc.).

Once I worked with this system because I was involved in a robotics project where we needed to develop visual perception capabilities for a mobile robot. We opted for monocular vision due to hardware limitations and cost, and were impressed by its versatility in object detection and motion tracking. However, we also experienced the inherent challenges, such as the need for more complex algorithms and additional computational resources to accurately infer depth from a single image. It was an enriching experience that taught us a lot about the strengths and limitations of monocular vision in robotics applications.

Monocular Vision in Robotics: Navigating Depth Perception Monocular vision, using a single camera, offers a distinct approach to depth perception in robotics, relying on cues like perspective and motion. It's invaluable for tasks like object recognition and pose tracking. However, challenges include ambiguity and the need for sophisticated algorithms. Despite this, advancements in machine learning show promise in overcoming these hurdles, pointing toward a future where monocular vision enhances robotic perception with clarity and precision.

In robotics, achieving precision involves advanced vision systems, with RGB-D vision standing out. Combining color (RGB) and depth (D) information, it enhances accuracy in object recognition, manipulation, 3D mapping, and human-robot interaction. Robots with RGB-D cameras excel in tasks like recognizing and manipulating objects based on appearance and depth, ensuring robust obstacle avoidance. This technology enhances human-robot interaction by accurately interpreting gestures. In manufacturing, it assesses product quality, examining visual characteristics and dimensional accuracy. Implementing RGB-D vision demands consideration of lighting and calibration for optimal performance, amplifying the accuracy and versatility of robotic systems.

RGB-D vision in robotics combines monocular and stereo vision with depth sensing capabilities, providing a more comprehensive understanding of the environment. It uses sensors that capture both RGB and depth information simultaneously, enhancing perception capabilities and depth perception. RGB-D directly provides depth, improving tasks like object manipulation, navigation, and 3D mapping. However, it has limitations such as cost, complexity, and sensitivity. Applications include object recognition, navigation, 3D mapping, and human-robot interaction. Despite these limitations, RGB-D vision has significant potential for real-world applications, making it a key technology in shaping the future of robotics.

Exploring RGB-D Vision in Robotics: Balancing Potential and Challenges RGB-D vision, utilizing a camera capturing both color (RGB) and depth (D) data, revolutionizes robotic perception. By projecting infrared patterns, it gauges depth via time-of-flight or phase shift. This technique enhances tasks like semantic segmentation and scene reconstruction. However, challenges include sensor quality dependency and susceptibility to interference and reflections. Despite this, ongoing advancements promise to bolster reliability, paving the way for more nuanced robotic interactions with the environment.

In recent years we have had our best experiences with LiDAR sensors in relation to 3D vision in robotic applications. The main advantages beyond the high accuracy is the insensitivity to extraneous light and sunlight. Furthermore, these sensors do not require any external light to work. With lidar sensors we are able not only to generate point clouds and thus create a 3D model, but also to determine the color of each individual point in space through the remission of the light. This also allows colors to be distinguished in the 3D model.

In robotics, LiDAR vision stands out as a highly effective system for achieving accurate results. LiDAR employs laser beams to measure distances, creating detailed 3D maps of the environment. This is invaluable for applications like autonomous vehicles, where it enables precise object detection, obstacle avoidance, and mapping. LiDAR finds applications in agriculture for crop monitoring, in forestry for terrain mapping, and in industrial automation for object detection and navigation. The benefits include high accuracy, long-range capability, and the ability to work in various lighting conditions. Despite its advantages, LiDAR systems can be costly, requiring careful consideration of the project's budget and specific requirements.

LiDAR and vision systems are essential for robots to perceive their environment. LiDAR uses pulsed lasers to measure distance to objects, creating a high-resolution 3D point cloud. It offers excellent accuracy and range, making it useful for mapping, navigation, localization, and object detection. Vision systems use cameras to capture images of the environment, analyzing them using computer vision algorithms. They provide rich information about texture, color, and depth, aiding in object recognition and manipulation. While LiDAR provides precise distance measurements, vision systems provide visual information. The combination of these technologies determines a robot's overall perception capabilities.

LiDAR's point cloud representation requires significant computational resources for processing and interpretation compared to camera images. LiDAR scanners suffer from multipath interference, where reflected light bounces off multiple surfaces before reaching the sensor, potentially distorting the captured data. While single-photon LiDAR mitigates this issue, it significantly increases cost and complexity.

In 2024, the best way to get started would be, 1) Use a LIDAR. As LIDARs have become exceptionally accurate and mostly a top choice for Localization and Mapping tasks. Disadvantage? LIDARs can be very expensive. 2) Alternately, having two RGB cameras and clever computer vision algorithms significantly reduces the cost and does not compromise on quality. One can easily find calibration and stereo vision algorithms online and get started.

Embedded Imaging: This involves integrating imaging systems directly onto the robotic platform, which simplifies the setup by eliminating the need for external computing resources. Embedded vision systems are compact and capable of performing high-quality depth perception and computing tasks autonomously. This technology is especially suitable for autonomous robots operating in dynamic environments such as warehouses, retail spaces, and healthcare facilities

Assessing your needs, priorities, and budget is essential to selecting the proper robotic vision system. Define work requirements, consider environment, accuracy, precision, real-time performance, and financial restrictions. Consider stereo, monocular, RGB-D, and LiDAR systems. Sensor specs, processing power, software features, integration simplicity, and technical support should be assessed. Scalability, power consumption, and safety certifications should also be considered. Researching systems, reading reviews, and testing them in real scenarios might help you decide. There is no "best" robotic vision system, therefore rigorous examination ensures robotic application success.

When comparing vision systems for a robotics project, the selection process should involve an assessment of various factors. First, the nature and complexity of the task at hand play a crucial role, determining the system's suitability for specific applications. The size, weight, and power consumption are essential considerations, especially for mobile or energy-sensitive robots. Cost and availability become pivotal, influencing the feasibility of integration into the project's budget. Evaluating accuracy, resolution, and robustness is vital to ensure the system meets performance expectations. Lastly, accounting for environmental conditions and constraints ensures the chosen vision system can operate effectively in the intended surroundings

I'm a strong advocate for the sensor fusion approach for several reasons. Firstly, it combines different types of sensors, leveraging their individual strengths to overcome individual limitations. This is crucial for real-world robot applications, especially in dynamic environments – both outdoors and indoors. Sensor fusion provides higher accuracy, robustness, and faster response times. While some advocate for a camera-only approach, it often requires significant time, money, and resources to achieve reliable results, especially in complex environments. Market trends also suggest that pure camera-based solutions are not yet the norm. Sensor fusion provides a more holistic and dependable approach.

Vision systems are key for robots! Here's a quick breakdown: Cognex: High-end leader, best for complex tasks but pricier. wenglor: Flexible, cost-effective option for diverse 2D applications. SICK: Rugged 2D & 3D for harsh environments, ideal for 3D tasks like bin-picking. Omron: User-friendly, affordable for simple inspection with their robots. Consider your task complexity, budget, and environment for the best fit!

Before debating which vision system is the most accurate, there needs to be an understanding of how "accuracy" is defined. Accuracy has different definitions in different scenarios/ applications such as positioning, object detection & tracking, gesture/ activity recognition to name a few. In the context of positioning, the localisation error (benchmarked against robust wheel encoder/ IMU readings) can be treated as the measure of accuracy. In case of object/ gesture detection, "No. of correct predictions/ Total no. of predictions" as done in a classification problem can be broadly taken as a measure of accuracy.

The definition of "accurate results" will vary depending on the application. Are we talking about an industrial robot doing pick and place? An AMR navigating in a warehouse? One general consideration would be that if you have already selected a robot for your application, sometimes looking at what vision systems are built-in or easy to integrate with this robot is a good starting point. Chances are, similar applications have been done before, and you could save time during integration (communications, tooling, calibration procedure).

When working on a robot project, consider how it sees things. Think about stereo, monocular, rgb-d, & lidar vision, & compare them. Explore infrared, thermal, panoramic views, & use computational techniques like neural networks. Consider stereo vision, which uses two eyes like humans; monocular vision, which uses one eye; rgb-d vision, which sees color & depth; lidar vision, which uses lasers; & even how to compare these vision systems. There's also structured light, time-of-flight, & even sonar or radar vision. You can explore different camera setups, 360-degree views, & real-time processing. Pay attention to sensor calibration & image pre-processing to choose best vision system for your robot.

Unfortunately There Is No YouTube for Robotics vision! The thing is that robust robot perception blends multiple sensors. Stereo vision aids in 3D structure mapping but falls short on flat areas; that's where LiDAR steps in, ensuring accurate distance measurements. Thermal imaging helps in low visibility, while hyperspectral cameras discern materials etc etc. But Even with the most sophisticated multimodal sensor systems need real-world data to truly understand an environment. As we're in the early stages of collecting such data (BMW and Figure Robotics deal), the process may take time. To accelerate learning, synthetic data generated by diffusion models could be the key, bridging the gap until sufficient real-world data is available.

An idealist viewpoint would identify a single system or a particular combination of multiple vision systems to give the "most" accuracy. The true choice would always lie in using a multi-system configuration, which further depends on the application and processing power. In my experience, I have used RGB-D cameras for identification of objects in a robot's environment, while I have used LiDAR for autonomous mobile robot navigation. I received good results in both my projects but a combination would provide a better result in either application. And, of course, a combination of other systems might provide an even better result.