• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

LiDAR Navigation

LiDAR is an autonomous navigation system that enables robots to understand their surroundings in an amazing way. It integrates laser scanning technology with an Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS) receiver to provide precise, detailed mapping data.

It's like a watch on the road, alerting the driver to potential collisions. It also gives the car the agility to respond quickly.

How LiDAR Works

LiDAR (Light-Detection and Range) makes use of laser beams that are safe for the eyes to look around in 3D. Onboard computers use this information to navigate the robot vacuum cleaner lidar and ensure security and accuracy.

LiDAR like its radio wave counterparts radar and sonar, detects distances by emitting laser waves that reflect off of objects. These laser pulses are recorded by sensors and used to create a live, 3D representation of the surrounding known as a point cloud. The superior sensing capabilities of LiDAR when in comparison to other technologies is built on the laser's precision. This creates detailed 2D and 3-dimensional representations of the surrounding environment.

ToF LiDAR sensors determine the distance of objects by emitting short pulses laser light and observing the time it takes the reflection signal to be received by the sensor. The sensor is able to determine the distance of an area that is surveyed by analyzing these measurements.

This process is repeated many times per second, creating a dense map in which each pixel represents an identifiable point. The resultant point clouds are typically used to determine the height of objects above ground.

The first return of the laser pulse, for instance, could represent the top surface of a building or tree, while the final return of the pulse represents the ground. The number of return times varies depending on the number of reflective surfaces that are encountered by a single laser pulse.

LiDAR can identify objects based on their shape and color. A green return, for instance can be linked to vegetation while a blue return could indicate water. In addition red returns can be used to determine the presence of an animal in the vicinity.

Another method of understanding LiDAR data is to use the information to create an image of the landscape. The topographic map is the most popular model that shows the heights and features of the terrain. These models can be used for many purposes including flood mapping, road engineering, inundation modeling, hydrodynamic modelling and coastal vulnerability assessment.

LiDAR is among the most important sensors used by Autonomous Guided Vehicles (AGV) because it provides real-time awareness of their surroundings. This permits AGVs to efficiently and safely navigate through difficult environments without the intervention of humans.

Sensors for LiDAR

LiDAR comprises sensors that emit and detect laser pulses, detectors that convert these pulses into digital information, and computer-based processing algorithms. These algorithms convert this data into three-dimensional geospatial pictures such as contours and building models.

When a beam of light hits an object, the energy of the beam is reflected and the system measures the time it takes for the pulse to travel to and return from the target. The system also identifies the speed of the object using the Doppler effect or by observing the change in the velocity of the light over time.

The resolution of the sensor's output is determined by the amount of laser pulses the sensor receives, as well as their strength. A higher speed of scanning can produce a more detailed output, while a lower scanning rate could yield more general results.

In addition to the sensor, other key components of an airborne LiDAR system include an GPS receiver that can identify the X,Y, and Z positions of the LiDAR unit in three-dimensional space, and an Inertial Measurement Unit (IMU) which tracks the device's tilt including its roll, pitch, and yaw. In addition to providing geo-spatial coordinates, IMU data helps account for the influence of the weather conditions on measurement accuracy.

There are two primary kinds of LiDAR scanners: solid-state and mechanical. Solid-state LiDAR, which includes technologies like Micro-Electro-Mechanical Systems and Optical Phase Arrays, operates without any moving parts. Mechanical LiDAR can achieve higher resolutions with technology like mirrors and lenses, but requires regular maintenance.

Based on the application they are used for, LiDAR scanners can have different scanning characteristics. For instance, high-resolution LiDAR can identify objects and their shapes and surface textures, while low-resolution LiDAR is mostly used to detect obstacles.

The sensitivity of a sensor can also affect how fast it can scan a surface and determine surface reflectivity. This is crucial for identifying surface materials and separating them into categories. LiDAR sensitivity can be related to its wavelength. This could be done to ensure eye safety or to prevent atmospheric spectrum characteristics.

LiDAR Range

The LiDAR range is the maximum distance that a laser is able to detect an object. The range is determined by the sensitivities of a sensor's detector and the intensity of the optical signals returned as a function of target distance. To avoid excessively triggering false alarms, many sensors are designed to block signals that are weaker than a specified threshold value.

The simplest method of determining the distance between the LiDAR sensor with an object is to look at the time difference between the moment that the laser beam is emitted and when it reaches the object's surface. This can be done using a clock attached to the sensor, or by measuring the duration of the pulse by using a photodetector. The resultant data is recorded as a list of discrete values, referred to as a point cloud which can be used for measuring, analysis, and navigation purposes.

A LiDAR scanner's range can be enhanced by using a different beam design and by changing the optics. Optics can be altered to alter the direction and the resolution of the laser beam detected. There are a myriad of aspects to consider when deciding on the best optics for a particular application such as power consumption and the ability to operate in a variety of environmental conditions.

While it's tempting promise ever-growing LiDAR range, it's important to remember that there are tradeoffs between achieving a high perception range and other system characteristics like frame rate, angular resolution and latency as well as object recognition capability. In order to double the detection range, a LiDAR must increase its angular resolution. This can increase the raw data as well as computational bandwidth of the sensor.

For instance, a LiDAR system equipped with a weather-robust head can determine highly detailed canopy height models even in poor conditions. This information, when combined with other sensor data, can be used to help recognize road border reflectors, making driving more secure and efficient.

LiDAR can provide information on many different objects and surfaces, including roads and even vegetation. Foresters, for instance can use LiDAR effectively to map miles of dense forest -- a task that was labor-intensive in the past and impossible without. This technology is helping to revolutionize industries such as furniture, paper and syrup.

LiDAR Trajectory

A basic LiDAR system consists of an optical range finder that what is lidar robot vacuum reflecting off the rotating mirror (top). The mirror lidar-based Vacuums rotates around the scene being digitized, in either one or two dimensions, scanning and recording distance measurements at specific angle intervals. The return signal is digitized by the photodiodes inside the detector and then filtering to only extract the desired information. The result is a digital cloud of data which can be processed by an algorithm to calculate platform position.

For instance, the trajectory of a drone that is flying over a hilly terrain calculated using the LiDAR point clouds as the robot vacuums with obstacle avoidance lidar travels through them. The data from the trajectory is used to steer the autonomous vehicle.

The trajectories generated by this system are extremely accurate for navigation purposes. Even in obstructions, they have low error rates. The accuracy of a trajectory is affected by several factors, including the sensitivity of the LiDAR sensors and the manner the system tracks motion.

The speed at which the lidar and INS produce their respective solutions is a crucial factor, as it influences both the number of points that can be matched and the number of times the platform needs to move itself. The stability of the system as a whole is affected by the speed of the INS.

The SLFP algorithm that matches the feature points in the point cloud of the lidar with the DEM that the drone measures, produces a better trajectory estimate. This is especially relevant when the drone is flying in undulating terrain with high pitch and roll angles. This is a significant improvement over the performance of traditional methods of integrated navigation using lidar and INS that use SIFT-based matching.

Another improvement is the creation of future trajectory for the sensor. This technique generates a new trajectory for each novel pose the LiDAR sensor is likely to encounter, instead of using a series of waypoints. The resulting trajectories are much more stable, and can be utilized by autonomous systems to navigate through rough terrain or in unstructured environments. The model behind the trajectory relies on neural attention fields to encode RGB images into a neural representation of the environment. Unlike the Transfuser approach that requires ground-truth training data about the trajectory, this method can be learned solely from the unlabeled sequence of LiDAR points.