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LiDAR and Robot Navigation

LiDAR is a crucial feature for mobile robots that need to navigate safely. It can perform a variety of functions, including obstacle detection and path planning.

2D lidar scans an area in a single plane, making it simpler and more efficient than 3D systems. This creates a powerful system that can recognize objects even if they're not perfectly aligned 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 environment. These systems determine distances by sending out pulses of light and analyzing the amount of time it takes for each pulse to return. The data is then compiled into an intricate, real-time 3D representation of the area being surveyed. This is known as a point cloud.

The precise sensing capabilities of LiDAR give robots an in-depth knowledge of their environment which gives them the confidence to navigate through various scenarios. Accurate localization is an important advantage, as LiDAR pinpoints precise locations by cross-referencing the data with maps that are already in place.

The LiDAR technology varies based on the application they are used for in terms of frequency (maximum range), resolution and horizontal field of vision. The basic principle of all LiDAR devices is the same that the sensor sends out an optical pulse that hits the surroundings and then returns to the sensor. This is repeated thousands per second, creating an immense collection of points that represent the surveyed area.

Each return point is unique, based on the composition of the surface object reflecting the light. For example buildings and trees have different reflectivity percentages than water or bare earth. Light intensity varies based on the distance and the scan angle of each pulsed pulse as well.

The data is then processed to create a three-dimensional representation - an image of a point cloud. This can be viewed by an onboard computer for navigational reasons. The point cloud can be filtered so that only the desired area is shown.

Or, the point cloud could be rendered in true color by matching the reflection of light to the transmitted light. This makes it easier to interpret the visual and more accurate spatial analysis. The point cloud can be labeled with GPS data, which permits precise time-referencing and temporal synchronization. This is beneficial for quality control and for time-sensitive analysis.

LiDAR is used in a variety of applications and industries. It is found on drones used for topographic mapping and for forestry work, as well as on autonomous vehicles to make an electronic map of their surroundings for safe navigation. It can also be utilized to measure the vertical structure of forests, helping researchers to assess the biomass and carbon sequestration capabilities. Other applications include monitoring the environment and detecting changes to atmospheric components like CO2 and greenhouse gasses.

Range Measurement Sensor

The core of the LiDAR device is a range measurement sensor that continuously emits a laser beam towards objects and surfaces. The pulse is reflected back and the distance to the object or surface can be determined by determining the time it takes the beam to reach the object and return to the sensor (or reverse). The sensor is typically mounted on a rotating platform, so that measurements of range are taken quickly across a 360 degree sweep. These two-dimensional data sets give a clear view of the robot's surroundings.

There are various types of range sensors and they all have different minimum and maximum ranges. They also differ in their field of view and resolution. KEYENCE has a range of sensors that are available and can help you select the right one for your requirements.

Range data is used to create two-dimensional contour maps of the operating area. It can be combined with other sensors, such as cameras or vision systems to enhance the performance and robustness.

The addition of cameras can provide additional information in visual terms to aid in the interpretation of range data and improve the accuracy of navigation. Some vision systems use range data to build a computer-generated model of environment, which can be used to direct the robot based on its observations.

It's important to understand the way a LiDAR sensor functions and what the system can accomplish. The robot can be able to move between two rows of crops and the goal is to determine the right one by using LiDAR data.

A technique called simultaneous localization and mapping (SLAM) can be used to accomplish this. SLAM is an iterative algorithm that makes use of the combination of existing conditions, like the robot's current location and orientation, as well as modeled predictions based on its current speed and heading, sensor data with estimates of noise and error quantities, and iteratively approximates the solution to determine the robot's location and local position. By using this method, the robot is able to navigate through complex and unstructured environments without the requirement for reflectors or other markers.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays a crucial part in a robot's ability to map its surroundings and local locate itself within it. Its development is a major research area in the field of artificial intelligence and mobile robotics. This paper reviews a range of the most effective approaches to solving the SLAM issues and discusses the remaining challenges.

The main goal of SLAM is to calculate the robot's movement patterns within its environment, while building a 3D map of that environment. The algorithms used in SLAM are based on features extracted from sensor data that could be laser or camera data. These features are identified by the objects or points that can be distinguished. These features could be as simple or complicated as a plane or corner.

The majority of Lidar sensors have a restricted field of view (FoV) which could limit the amount of information that is available to the SLAM system. A larger field of view permits the sensor to capture an extensive area of the surrounding environment. This could lead to an improved navigation accuracy and a complete mapping of the surroundings.

To accurately determine the robot's location, the SLAM must match point clouds (sets in the space of data points) from the current and the previous environment. There are many algorithms that can be used for this purpose such as iterative nearest point and normal distributions transform (NDT) methods. These algorithms can be used in conjunction with sensor data to produce a 3D map that can later be displayed as an occupancy grid or 3D point cloud.

A SLAM system is extremely complex and requires substantial processing power to operate efficiently. This poses problems for robotic systems which must be able to run in real-time or on a limited hardware platform. To overcome these issues, a SLAM can be adapted to the sensor hardware and software environment. For instance a laser sensor with an extremely high resolution and a large FoV may require more resources than a less expensive low-resolution scanner.

Map Building

A map is a representation of the environment that can be used for a variety of reasons. It is typically three-dimensional, and serves a variety of functions. It could be descriptive (showing exact locations of geographical features to be used in a variety applications like street maps) as well as exploratory (looking for patterns and connections among phenomena and their properties in order to discover deeper meaning in a given subject, like many thematic maps), or even explanatory (trying to convey details about the process or object, often through visualizations like graphs or illustrations).

Local mapping utilizes the information that LiDAR sensors provide at the base of the robot slightly above ground level to build a two-dimensional model of the surrounding. To accomplish this, the sensor gives distance information from a line sight of each pixel in the two-dimensional range finder which allows topological models of the surrounding space. This information is used to develop typical navigation and segmentation algorithms.

Scan matching is the algorithm that utilizes the distance information to compute an estimate of the position and orientation for the AMR for each time point. This is accomplished by minimizing the gap between the robot vacuum cleaner with lidar's anticipated future state and its current condition (position or rotation). Scanning matching can be achieved using a variety of techniques. Iterative Closest Point is the most well-known, and has been modified many times over the years.

Another way to achieve local map creation is through Scan-to-Scan Matching. This is an incremental method that is used when the AMR does not have a map or the map it has is not in close proximity to the current environment due changes in the surroundings. This technique is highly vulnerable to long-term drift in the map because the accumulation of pose and position corrections are subject to inaccurate updates over time.

A multi-sensor fusion system is a robust solution that utilizes multiple data types to counteract the weaknesses of each. This type of system is also more resilient to errors in the individual sensors and can deal with the dynamic environment that is constantly changing.