20 Things You Need To Know About Lidar Robot Navigation
LiDAR and Robot Navigation
LiDAR is a vital capability for mobile robots who need to be able to navigate in a safe manner. It can perform a variety of functions, such as obstacle detection and route planning.
2D lidar scans the surrounding in one plane, which is much simpler and cheaper than 3D systems. This makes for a more robust system that can detect obstacles even if they aren't aligned exactly with the sensor plane.
LiDAR Device
LiDAR (Light Detection and Ranging) sensors use eye-safe laser beams to "see" the environment around them. These sensors determine distances by sending out pulses of light, and measuring the time it takes for each pulse to return. The data is then processed to create a 3D, real-time representation of the surveyed region called a "point cloud".
The precise sensing prowess of LiDAR gives robots an understanding of their surroundings, empowering them with the confidence to navigate diverse scenarios. Accurate localization is an important strength, as the technology pinpoints precise locations by cross-referencing the data with existing maps.
LiDAR devices differ based on the application they are used for in terms of frequency (maximum range), resolution and horizontal field of vision. However, the basic principle is the same across all models: the sensor transmits a laser pulse that hits the environment around it 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 surveyed area.
Each return point is unique, based on the surface object that reflects the pulsed light. Buildings and trees for instance have different reflectance percentages than the bare earth or water. The intensity of light also differs based on the distance between pulses and the scan angle.
This data is then compiled into a detailed, three-dimensional representation of the surveyed area which is referred to as a point clouds which can be seen on an onboard computer system to aid in navigation. The point cloud can be filtered so that only the area that is desired is displayed.
The point cloud could be rendered in a true color by matching the reflection of light to the transmitted light. This allows for a more accurate visual interpretation and an improved spatial analysis. The point cloud can be tagged with GPS information that allows for accurate time-referencing and temporal synchronization which is useful for quality control and time-sensitive analyses.
LiDAR is used in a wide range of applications and industries. It is used on drones for topographic mapping and forestry work, and on autonomous vehicles to create an electronic map of their surroundings for safe navigation. It is also used to measure the vertical structure of forests which allows researchers to assess biomass and carbon storage capabilities. Other applications include environmental monitors and detecting changes to atmospheric components like CO2 and greenhouse gasses.
Range Measurement Sensor
The core of LiDAR devices is a range measurement sensor that emits a laser beam towards surfaces and objects. The laser beam is reflected and the distance can be measured by observing the amount of time it takes for the laser pulse to reach the surface or object and then return to the sensor. The sensor is usually mounted on a rotating platform to ensure that measurements of range are made quickly over a full 360 degree sweep. These two-dimensional data sets offer an exact picture of the robot’s surroundings.
There are many different types of range sensors. They have varying minimum and maximum ranges, resolutions and fields of view. KEYENCE has a range of sensors that are available and can help you select the most suitable one for your needs.
Range data can be used to create contour maps within two dimensions of the operational area. It can be paired with other sensors like cameras or vision system to improve the performance and durability.
The addition of cameras can provide additional visual data to assist in the interpretation of range data and improve navigational accuracy. Some vision systems use range data to construct a computer-generated model of environment, which can be used to direct a robot based on its observations.
To get the most benefit from the LiDAR system it is essential to have a good understanding of how the sensor works and what it is able to do. Most of the time the robot moves between two rows of crop and the goal is to find the correct row by using the LiDAR data sets.
A technique called simultaneous localization and mapping (SLAM) is a method to achieve this. SLAM is an iterative algorithm that uses a combination of known conditions, such as the robot's current location and orientation, modeled forecasts using its current speed and direction sensor data, estimates of error and noise quantities, and iteratively approximates the solution to determine the robot's position and position. With this method, the robot can move through unstructured and complex environments without the necessity of reflectors or other markers.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm is the key to a robot's ability create a map of its surroundings and locate its location within that map. Its evolution is a major research area for the field of artificial intelligence and mobile robotics. This paper examines a variety of leading approaches to solving the SLAM problem and describes the problems that remain.
The main objective of SLAM is to determine the robot's sequential movement in its environment while simultaneously creating a 3D model of the environment. The algorithms of SLAM are based upon characteristics taken from sensor data which could be laser or camera data. These features are defined as objects or points of interest that are distinct from other objects. They could be as simple as a corner or plane or more complex, for instance, shelving units or pieces of equipment.

Most Lidar sensors only have limited fields of view, which could restrict the amount of information available to SLAM systems.
vacuum robot lidar of view permits the sensor to record an extensive area of the surrounding environment. This could lead to an improved navigation accuracy and a more complete map of the surrounding.
In order to accurately estimate the robot's position, the SLAM algorithm must match point clouds (sets of data points scattered across space) from both the previous and present 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 an 3D map that can be displayed as an occupancy grid or 3D point cloud.
A SLAM system can be complex and require a significant amount of processing power in order to function efficiently. This is a problem for robotic systems that require to perform in real-time, or run on a limited hardware platform. To overcome these issues, the SLAM system can be optimized for the particular sensor software and hardware. For example a laser scanner with an extremely high resolution and a large FoV may require more processing resources than a less expensive, lower-resolution scanner.
Map Building
A map is a representation of the environment generally in three dimensions, which serves many purposes. It could be descriptive (showing the precise location of geographical features that can be used in a variety of ways such as a street map) as well as exploratory (looking for patterns and connections between various phenomena and their characteristics to find deeper meanings in a particular subject, like many thematic maps) or even explanatory (trying to communicate information about the process or object, often using visuals, such as graphs or illustrations).
Local mapping makes use of the data generated by LiDAR sensors placed at the base of the robot slightly above ground level to construct a 2D model of the surroundings. To do this, the sensor provides distance information from a line sight to each pixel of the two-dimensional range finder, which permits topological modeling of the surrounding space. Most navigation and segmentation algorithms are based on this information.
Scan matching is an algorithm that uses distance information to determine the orientation and position of the AMR for each point. This is accomplished by reducing the error of the robot's current condition (position and rotation) and the expected future state (position and orientation). There are a variety of methods to achieve scan matching. Iterative Closest Point is the most popular, and has been modified numerous times throughout the years.
Scan-toScan Matching is another method to create a local map. This algorithm works when an AMR doesn't have a map or the map it does have does not match its current surroundings due to changes. This method is extremely susceptible to long-term drift of the map because the accumulated position and pose corrections are subject to inaccurate updates over time.
A multi-sensor Fusion system is a reliable solution that makes use of various data types to overcome the weaknesses of each. This kind of navigation system is more resilient to errors made by the sensors and is able to adapt to dynamic environments.