LiDAR and Robot Navigation

LiDAR is among the essential capabilities required for mobile robots to safely navigate. It comes with a range of functions, such as obstacle detection and route planning.

2D lidar scans the surrounding in one plane, which is simpler and more affordable than 3D systems. This makes for an improved system that can detect obstacles even if they aren't aligned with the sensor plane.

lidar navigation Device

LiDAR sensors (Light Detection And Ranging) make use of laser beams that are safe for the eyes to "see" their environment. By sending out light pulses and measuring the amount of time it takes for each returned pulse the systems are able to calculate distances between the sensor and the objects within its field of vision. This data is then compiled into an intricate 3D model that is real-time and in real-time the area that is surveyed, referred to as a point cloud.

LiDAR's precise sensing ability gives robots a deep understanding of their surroundings and gives them the confidence to navigate through various situations. Accurate localization is a particular strength, as the technology pinpoints precise positions using cross-referencing of data with existing maps.

Depending on the application, Lidar robot devices can vary in terms of frequency and range (maximum distance), resolution, and horizontal field of view. The basic principle of all lidar robot devices is the same: the sensor sends out a laser pulse which hits the surrounding area and then returns to the sensor. This process is repeated thousands of times every second, resulting in an enormous collection of points that make up the area that is surveyed.

Each return point is unique depending on the surface object reflecting the pulsed light. Trees and buildings for instance have different reflectance levels as compared to the earth's surface or water. The intensity of light is dependent on the distance and the scan angle of each pulsed pulse as well.

The data is then assembled into a detailed three-dimensional representation of the surveyed area - called a point cloud which can be seen on an onboard computer system to aid in navigation. The point cloud can be reduced to show only the desired area.

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

LiDAR is employed in a variety of industries and applications. It is utilized on drones to map topography and for forestry, and on autonomous vehicles which create a digital map for safe navigation. It is also used to determine the vertical structure of forests, which helps researchers to assess the carbon sequestration capacities and biomass. Other applications include monitoring the environment and monitoring changes to atmospheric components such as CO2 or greenhouse gasses.

Range Measurement Sensor

The heart of LiDAR devices is a range measurement sensor that continuously emits a laser pulse toward surfaces and objects. The laser beam is reflected and the distance can be determined by measuring the time it takes for the laser's pulse to reach the surface or object and then return to the sensor. Sensors are mounted on rotating platforms that allow rapid 360-degree sweeps. Two-dimensional data sets provide an accurate picture of the robot vacuum lidar’s surroundings.

There are various types of range sensors and all of them have different minimum and maximum ranges. They also differ in the resolution and field. KEYENCE offers a wide variety of these sensors and will help you choose the right solution for your needs.

Range data is used to generate two-dimensional contour maps of the operating area. It can be paired with other sensors such as cameras or vision system to increase the efficiency and robustness.

The addition of cameras can provide additional information in visual terms to aid in the interpretation of range data and increase navigational accuracy. Some vision systems are designed to utilize range data as an input to computer-generated models of the environment that can be used to guide the robot according to what it perceives.

It is important to know the way a LiDAR sensor functions and what the system can accomplish. The robot is often able to move between two rows of plants and the goal is to determine the right one by using LiDAR data.

A technique called simultaneous localization and mapping (SLAM) can be employed to accomplish this. SLAM is an iterative algorithm which makes use of an amalgamation of known circumstances, such as the robot's current position and orientation, modeled predictions that are based on the current speed and heading sensors, and estimates of noise and error quantities, and iteratively approximates a solution to determine the robot vacuum with obstacle avoidance lidar's location and its pose. By using this method, the robot will be able to navigate through complex and unstructured environments without the need for reflectors or other markers.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays a key role in a robot's capability to map its environment and locate itself within it. Its evolution is a major research area for robots with artificial intelligence and mobile. This paper surveys a variety of current approaches to solving the SLAM problem and discusses the challenges that remain.

The primary goal of SLAM is to determine the robot's sequential movement within its environment, while creating a 3D model of the surrounding area. SLAM algorithms are built upon features derived from sensor data, which can either be laser or camera data. These characteristics are defined by objects or points that can be identified. These can be as simple or complex as a plane or corner.

Most Lidar sensors have an extremely narrow field of view, which may limit the information available to SLAM systems. A wider field of view permits the sensor to capture more of the surrounding environment. This can lead to a more accurate navigation and a more complete map of the surrounding.

In order to accurately determine the robot's location, the SLAM algorithm must match point clouds (sets of data points in space) from both the current and previous environment. This can be accomplished by using a variety of algorithms such as the iterative nearest point and normal distributions transformation (NDT) methods. These algorithms can be paired with sensor data to create an 3D map that can later be displayed as an occupancy grid or 3D point cloud.

A SLAM system can be a bit complex and require significant amounts of processing power to function efficiently. This can be a problem for robotic systems that have to achieve real-time performance, or run on the hardware of a limited platform. To overcome these issues, a SLAM system can be optimized to the specific software and hardware. For example a laser scanner that has a an extensive FoV and high resolution could require more processing power than a less scan with a lower resolution.

Map Building

A map is an illustration of the surroundings generally in three dimensions, and serves many purposes. It could be descriptive, indicating the exact location of geographical features, for use in various applications, such as the road map, or an exploratory, looking for patterns and relationships between phenomena and their properties to uncover deeper meaning to a topic like thematic maps.

Local mapping creates a 2D map of the surrounding area using data from LiDAR sensors that are placed at the bottom of a vacuum robot with lidar, a bit above the ground level. This is accomplished by the sensor providing distance information from the line of sight of every one of the two-dimensional rangefinders which permits topological modelling of the surrounding space. The most common segmentation and navigation algorithms are based on this data.

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 minimizing the gap between the robot's future state and its current state (position or rotation). Scanning matching can be achieved with a variety of methods. Iterative Closest Point is the most popular technique, and has been tweaked numerous times throughout the years.

Scan-toScan Matching is another method to build a local map. This is an incremental method that is employed when the AMR does not have a map, or the map it has does not closely match its current surroundings due to changes in the environment. This method is extremely susceptible to long-term map drift 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 different types of data to overcome the weaknesses of each. This kind of system is also more resilient to errors in the individual sensors and can cope with environments that are constantly changing.