LiDAR and Robot Navigation

LiDAR is a crucial feature for mobile robots that need to travel in a safe way. It can perform a variety of capabilities, including obstacle detection and route planning.

2D lidar scans the surrounding in one plane, which is much simpler and more affordable than 3D systems. This creates a powerful system that can identify objects even if they're completely aligned with the sensor plane.

LiDAR Device

LiDAR (Light detection and Ranging) sensors make use of eye-safe laser beams to "see" the surrounding environment around them. By transmitting pulses of light and measuring the amount of time it takes to return each pulse the systems can determine distances between the sensor and objects in its field of view. The data is then compiled to create a 3D, real-time representation of the area surveyed known as a "point cloud".

LiDAR's precise sensing ability gives robots a thorough knowledge of their environment, giving them the confidence to navigate through various situations. The technology is particularly adept at determining precise locations by comparing data with maps that exist.

LiDAR devices vary depending on the application they are used for in terms of frequency (maximum range) and resolution, as well as horizontal field of vision. The principle behind all LiDAR devices is the same that the sensor emits the laser pulse, which is absorbed by the surroundings and then returns to the sensor. This is repeated a thousand times per second, creating an enormous number of points that represent the area that is surveyed.

Each return point is unique depending on the surface object that reflects the pulsed light. Trees and buildings for instance have different reflectance levels than bare earth or water. The intensity of light differs based on the distance between pulses and the scan angle.

The data is then assembled into an intricate, three-dimensional representation of the surveyed area known as a point cloud which can be seen on an onboard computer system to aid in navigation. The point cloud can be filtered so that only the area you want to see is shown.

Or, the point cloud could be rendered in a true color by matching the reflection light to the transmitted light. This results in a better visual interpretation and a more accurate spatial analysis. The point cloud can be tagged with GPS data that can be used to ensure accurate time-referencing and temporal synchronization. This is useful to ensure quality control, and for time-sensitive analysis.

LiDAR is used in a variety of industries and applications. It is used on drones that are used for topographic mapping and forestry work, as well as on autonomous vehicles to create a digital map of their surroundings for safe navigation. It can also be used to determine the vertical structure of forests, helping researchers evaluate biomass and carbon sequestration capabilities. Other applications include monitoring environmental conditions and monitoring changes in atmospheric components such as CO2 or greenhouse gases.

Range Measurement Sensor

The core of the LiDAR device is a range sensor that continuously emits a laser beam towards objects and surfaces. The laser pulse is reflected, and the distance to the surface or object can be determined by determining the time it takes for the beam to be able to reach the object before returning to the sensor (or the reverse). The sensor is usually placed on a rotating platform so that measurements of range are taken quickly across a 360 degree sweep. Two-dimensional data sets provide an accurate image of the robot's surroundings.

There are many different types of range sensors. They have varying minimum and maximum ranges, resolution and field of view. KEYENCE offers a wide variety of these sensors and can assist you in choosing the best solution for your application.

Range data can be used to create contour maps in two dimensions of the operating area. It can be combined with other sensors, such as cameras or vision system to enhance the performance and durability.

The addition of cameras provides additional visual data that can assist with the interpretation of the range data and improve the accuracy of navigation. Certain vision systems utilize range data to construct a computer-generated model of the environment, which can then be used to guide the robot based on its observations.

It is essential to understand the way a LiDAR sensor functions and what the system can do. In most cases, the robot is moving between two crop rows and the goal is to determine the right row using the LiDAR data set.

To achieve this, a method known as simultaneous mapping and localization (SLAM) may be used. SLAM is an iterative algorithm that uses the combination of existing circumstances, such as the robot vacuum with lidar's current position and orientation, as well as modeled predictions based on its current speed and heading sensors, and estimates of error and noise quantities, and iteratively approximates a solution to determine the robot's location and position. By using this method, the robot can navigate in complex and unstructured environments without the need 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 locate itself within it. The evolution of the algorithm is a key research area for robots with artificial intelligence and mobile. This paper examines a variety of current approaches to solving the SLAM problem and outlines the problems that remain.

SLAM's primary goal is to determine the sequence of movements of a robot in its environment while simultaneously constructing an 3D model of the environment. The algorithms used in SLAM are based on features extracted from sensor information that could be camera or laser data. These features are defined by the objects or points that can be distinguished. They can be as simple as a corner or plane or more complex, like shelving units or pieces of equipment.

The majority of Lidar robot navigation sensors have limited fields of view, which may restrict the amount of information available to SLAM systems. A wide FoV allows for the sensor to capture a greater portion of the surrounding environment which could result in an accurate map of the surroundings and a more accurate navigation system.

To accurately determine the location of the robot, a SLAM must match point clouds (sets of data points) from the current and the previous environment. This can be achieved by using a variety of algorithms that include the iterative closest point and normal distributions transformation (NDT) methods. These algorithms can be combined with sensor data to create a 3D map of the surrounding, which can be displayed as an occupancy grid or a 3D point cloud.

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

Map Building

A map is an illustration of the surroundings usually in three dimensions, which serves many purposes. It can be descriptive (showing accurate location of geographic features that can be used in a variety applications such as street maps) or exploratory (looking for patterns and relationships between phenomena and their properties, to look for deeper meanings in a particular topic, as with many thematic maps) or even explanatory (trying to communicate details about the process or object, often through visualizations like graphs or illustrations).

Local mapping makes use of the data provided by LiDAR sensors positioned on the bottom of the robot just above the ground to create a 2D model of the surroundings. This is done by the sensor providing distance information from the line of sight of every pixel of the rangefinder in two dimensions that allows topological modeling of the surrounding space. This information is used to create typical navigation and segmentation algorithms.

Scan matching is the algorithm that takes advantage of the distance information to calculate an estimate of orientation and position for the AMR for each time point. This is done by minimizing the error of the robot's current state (position and rotation) and its anticipated future state (position and orientation). A variety of techniques have been proposed to achieve scan matching. Iterative Closest Point is the most popular technique, and has been tweaked numerous times throughout the time.

Scan-toScan Matching is another method to achieve local map building. This algorithm works when an AMR does not have a map or the map it does have does not coincide with its surroundings due to changes. This method is extremely vulnerable to long-term drift in the map, as the accumulation of pose and position corrections are susceptible to inaccurate updates over time.

To address this issue To overcome this problem, a multi-sensor navigation system is a more robust approach that takes advantage of multiple data types and overcomes the weaknesses of each of them. This kind of navigation system is more resilient to the erroneous actions of the sensors and can adapt to changing environments.