In a complex and versatile environment, assistive automatic driving technology better identifies the actual road, vehicle location and obstacle information, real-time sensory driving risk, and achieves predetermined goals for intelligent driving and automatic parking.
Lidar is a method of determining distances (varying distances) by aiming an object with a laser and measuring the time it takes for the reflected light to return to the receiver. Lidar can also be used to present digital 3D representations of regions of the Earth and the ocean floor due to differences in laser return times and by varying laser wavelengths. It has terrestrial, airborne and mobile applications.
Lidar has a smaller wavelength than microwaves and a narrower radius.
Lidar has the following advantages:
1. High Angle Resolution, Fast Resolution and High Range Resolution. High-resolution and clear images of moving subjects can be obtained using Range-Doppler imaging technology.
2. It has strong anti-jamming ability and good hiding; Low probability of laser capture.
Due to its short wavelength, lidar is able to detect targets at the molecular level.
4. In the case of the same function, it is smaller and lighter than a microwave radar.
Lidar has the following disadvantages:
1. Atmosphere and weather greatly affect the laser, and bad weather reduces the operating distance. In addition, atmospheric turbulence reduces measurement accuracy.
2. Narrow laser beam makes it difficult to find and capture objects. Generally, lidar is used to accurately track and measure an object after a fast and rough object from other devices.
In vehicle detection systems, it is very important to improve the accuracy of object identification, tracking, obstacle detection and accurate positioning. Whether an automatic driving vehicle can adjust the driving depends on the exact detection capability of the sensor system and the central decision-making system. Therefore, driving environment data is of great importance for an automatic driving environment detection system.
We can use lidar to sparsely describe a three-dimensional world, and the obstacle point cloud obtained by scanning is usually denser than the background. We can use it to detect barriers through classification and clusters.
With the breakthrough in identification and segmentation technology, lidar has been able to detect pedestrians and vehicles effectively, produce a 3D bounding box or 3D point cloud, and even tried to use lidar to detect lane lines on the ground.
Of the environmental monitoring sensors, ultrasonic radar is mainly used for radar reversal and short-range monitoring in automatic parking. The camera, millimeter wave radar and lidar are widely used in ADAS operations.
The detection parameters of the four types of sensors are different, such as detection range, resolution, angle resolution, and so on, corresponding to the advantages and disadvantages of object detection, detection and classification ability, three-dimensional modeling, anti-antenna. bad weather and so on. All kinds of sensors can provide good complementary benefits, and sensor fusion has become the mainstream choice in today’s self-driving industry.
More information: Sensor fusion classes
3D Point Cloud is a collaboration of numerous points (data points) that has spread to a 3D space where data points are collected using sensors such as Lidar. The sensors emit light and count the time it takes to bounce back to the sensor to create each point. The collected points are combined to form a complete picture, as shown below.
3D Point Cloud is widely used for product development and analysis in areas related to architecture, the aerospace industry, driving, transportation, medical devices, consumer goods, and more. Potential uses and applications are only expected to increase in the future.
3D point cloud image note based on Lidar sensors
3D point cloud-coded data is the basics of driverless technology.
3D cloud marking is considered to be the most suitable for accurate identification via Lidar sensors.
3D point cloud image tagging refers to the tagging of a target object in a 3D image collected by lidar sensors using 3D boxing. The target includes vehicles, pedestrians, traffic signs and trees, etc.
TavuSilta is human and ML powered data entry tool platform. We provide scalable, high-quality data for the ML / AI industry with a flexible workflow.
Quality and accuracy
- ML-assisted capacity can help reduce human error automatically advance markings
- real-time quality control and quality control integrated into the labeling workflow when a consensus mechanism is put in place to ensure accuracy.
- Understanding – Assign the same task to multiple employees, and the correct answer is the one that comes back from the majority output.
- All the results of the work has been fully security checked and inspected machine and manpower.
In this way, ByteBridge can confirm the acceptance and accuracy of the data over 98%
- Preview progress: the customer can monitor the progress of the entries in real time on the dashboard
- Preview the result: the customer can get the results in real time on the dashboard
Real-time outputs: the customer can receive real-time output via the API. We support JSON, XML, CSV, etc., and we can provide editable data type to meet your needs.
3D Point Cloud Markup Service
ByteBridge has self-developed 3D Point Cloud labels, a quality control tool, and label functions that can complement high-quality and accurate 3D point cloud labels for 2D-3D fusion or 3D images provided by various manufacturers and devices, and provide a single station management service, labels, quality control and quality control.
3D point cloud annotation types:
- Sensor Fusion Cuboids: 12 categories include car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
- Sensor fusion segmentation: barrier classification, different types of band specifications
- Monitoring of sensor fusion cuboids
① Tracking the same object with the same ID, marking the initial state;
② Dot clouds or time-aligned images can be output, only dot cloud outputs.
Benefits of the 3D Point Cloud service:
- Supports 2D, 3D mapping, multiple cameras
- Supports a large number of data entries
- Supports continuous monitoring of the framework
- Support the management of labeling, quality control and approval.
- Support pre-authentication
The collaboration of human and labor and artificial intelligence algorithms ensures a 50% lower price compared to the normal market.
Because the quality of the marked data sets determines the success of the self-driving industry, collaboration reliable partner can help developers overcome data labeling challenges.
We can offer personal marking tools and services according to customer requirements.
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