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

LiDAR and Robot Navigation

LiDAR is a crucial feature for mobile robots that require to navigate safely. It can perform a variety of functions, such as obstacle detection and route planning.

2D lidar scans an environment in a single plane, making it easier and more cost-effective compared to 3D systems. This makes for an enhanced system that can identify obstacles even when they aren't aligned exactly with the sensor plane.

LiDAR Device

LiDAR sensors (Light Detection And Ranging) make use of laser beams that are safe for the eyes to "see" their surroundings. They determine distances by sending out pulses of light and analyzing the time it takes for each pulse to return. The information is then processed into an intricate, real-time 3D representation of the area that is surveyed, referred to as a point cloud.

The precise sensing capabilities of LiDAR allows robots to have an knowledge of their surroundings, empowering them with the confidence to navigate through various scenarios. The technology is particularly good at pinpointing precise positions by comparing data with existing maps.

Based on the purpose the LiDAR device can differ in terms of frequency and range (maximum distance), resolution, and horizontal field of view. The principle behind all LiDAR devices is the same that the sensor sends out a laser pulse which hits the surroundings and then returns to the sensor. This is repeated a thousand times per second, leading to an enormous number of points that make up the area that is surveyed.

Each return point is unique, based on the surface object that reflects the pulsed light. For instance buildings and trees have different reflectivity percentages than bare earth or water. The intensity of light also differs based on the distance between pulses as well as the scan angle.

The data is then compiled to create a three-dimensional representation. the point cloud, which can be viewed by an onboard computer to aid in navigation. The point cloud can be filterable so that only the area you want to see is shown.

The point cloud can be rendered in color by matching reflect light with transmitted light. This allows for better visual interpretation and more precise analysis of spatial space. The point cloud may also be tagged with GPS information, which provides temporal synchronization and accurate time-referencing, useful for quality control and time-sensitive analysis.

lidar sensor robot vacuum is employed in a wide range of industries and applications. It can be found on drones that are used for topographic mapping and for forestry work, and on autonomous vehicles that create a digital map of their surroundings for safe navigation. It can also be utilized to assess the vertical structure of forests which allows researchers to assess the carbon storage capacity of biomass and carbon sources. Other uses include environmental monitoring and detecting changes in atmospheric components like CO2 or greenhouse gases.

Range Measurement Sensor

A LiDAR device is a range measurement system that emits laser beams repeatedly towards surfaces and objects. The laser pulse is reflected and the distance can be determined by observing the time it takes for the laser's pulse to be able to reach the object's surface and then return to the sensor. The sensor is usually mounted on a rotating platform so that measurements of range are made quickly over a full 360 degree sweep. Two-dimensional data sets offer a complete overview of the robot's surroundings.

There are different types of range sensors and they all have different ranges for minimum and maximum. They also differ in the resolution and field. KEYENCE offers a wide range of sensors available and can assist you in selecting the best budget lidar robot vacuum one for your application.

Range data can be used to create contour maps in two dimensions of the operational area. It can be paired with other sensor technologies such as cameras or vision systems to improve efficiency and the robustness of the navigation system.

The addition of cameras adds additional visual information that can assist in the interpretation of range data and improve accuracy in navigation. Some vision systems use range data to create a computer-generated model of the environment, which can be used to guide the robot based on its observations.

To get the most benefit from the LiDAR system it is crucial to be aware of how the sensor operates and what it can accomplish. In most cases, the robot is moving between two crop rows and the aim is to identify the correct row by using the LiDAR data set.

A technique known as simultaneous localization and mapping (SLAM) can be used to achieve this. SLAM is an iterative algorithm that uses a combination of known circumstances, such as the robot's current location and orientation, modeled predictions using its current speed and heading sensor data, estimates of noise and error quantities, and iteratively approximates the solution to determine the robot's position and pose. This technique allows the robot to move in unstructured and complex environments without the need for markers or reflectors.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm is key to a robot vacuum with lidar and camera's ability create a map of their environment and pinpoint its location within the map. The evolution of the algorithm has been a key research area for the field of artificial intelligence and mobile robotics. This paper reviews a variety of leading approaches for solving the SLAM problems and highlights the remaining problems.

The main objective of SLAM is to estimate the robot's sequential movement in its environment while simultaneously creating a 3D map of the surrounding area. The algorithms used in SLAM are based upon features derived from sensor data, which can either be camera or laser data. These features are defined by points or objects that can be distinguished. These features can be as simple or complex as a corner or plane.

Most Lidar sensors have only an extremely narrow field of view, which can restrict the amount of data that is available to SLAM systems. A wide FoV allows for the sensor to capture more of the surrounding environment, which can allow for a more complete map of the surroundings and a more accurate navigation system.

To accurately estimate the location of the robot, the SLAM must be able to match point clouds (sets of data points) from both the current and the previous environment. This can be done using a number of algorithms such as the iterative nearest point and normal distributions transformation (NDT) methods. These algorithms can be paired with sensor data to produce a 3D map that can be displayed as an occupancy grid or 3D point cloud.

A SLAM system can be complex and require significant amounts of processing power in order to function efficiently. This can present challenges for robotic systems that must perform in real-time or on a limited hardware platform. To overcome these issues, the SLAM system can be optimized to the particular sensor software and hardware. For example, a laser sensor with an extremely high resolution and a large FoV may require more resources than a lower-cost low-resolution scanner.

Map Building

A map is an illustration of the surroundings generally in three dimensions, that serves many purposes. It can be descriptive, displaying the exact location of geographical features, and is used in a variety of applications, such as the road map, or exploratory searching for patterns and connections between phenomena and their properties to find deeper meaning in a subject like many thematic maps.

Local mapping utilizes the information provided by LiDAR sensors positioned at the bottom of the robot vacuum with lidar and camera slightly above ground level to build a 2D model of the surroundings. To do this, the sensor will provide distance information derived from a line of sight of each pixel in the range finder in two dimensions, which allows for topological modeling of the surrounding space. Most segmentation and navigation algorithms are based on this data.

Scan matching is an algorithm that makes use of distance information to estimate the location and orientation of the AMR for every time point. This is achieved by minimizing the differences between the robot's expected future state and its current condition (position or rotation). Scanning matching can be achieved with a variety of methods. Iterative Closest Point is the most popular, and has been modified many times over the time.

Another approach to local map creation is through Scan-to-Scan Matching. This algorithm works when an AMR does not have a map or the map it does have does not match its current surroundings due to changes. This method is vulnerable to long-term drifts in the map, since the cumulative corrections to location and pose are susceptible to inaccurate updating over time.

To address this issue, a multi-sensor fusion navigation system is a more robust solution that utilizes the benefits of different types of data and counteracts the weaknesses of each of them. This type of system is also more resistant to errors in the individual sensors and is able to deal with the dynamic environment that is constantly changing.