LiDAR and Robot Navigation

LiDAR is one of the central capabilities needed for mobile robots to safely navigate. It has a variety of functions, such as obstacle detection and route planning.

2D lidar scans the surrounding in a single plane, which is simpler and more affordable than 3D systems. This creates a powerful system that can identify objects even when they aren't completely 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 surroundings. By transmitting pulses of light and measuring the amount of time it takes for each returned pulse they can calculate distances between the sensor and objects within their field of view. This data is then compiled into an intricate 3D model that is real-time and in real-time the surveyed area known as a point cloud.

The precise sense of LiDAR provides robots with an understanding of their surroundings, empowering them with the confidence to navigate through various scenarios. Accurate localization is a major benefit, since the technology pinpoints precise locations by cross-referencing the data with maps that are already in place.

Depending on the use the LiDAR device can differ in terms of frequency, range (maximum distance), resolution, and horizontal field of view. The basic principle of all LiDAR devices is the same that the sensor sends out the laser pulse, which is absorbed by the surroundings and then returns to the sensor. The process repeats thousands of times per second, creating an immense collection of points representing the surveyed area.

Each return point is unique based on the structure of the surface reflecting the pulsed light. Buildings and trees, for example, have different reflectance percentages than the bare earth or water. The intensity of light varies with the distance and scan angle of each pulsed pulse as well.

The data is then compiled to create a three-dimensional representation, namely an image of a point cloud. This can be viewed by an onboard computer for navigational reasons. The point cloud can be filtering to show only the area you want to see.

The point cloud can be rendered in true color by matching the reflected light with the transmitted light. This allows for better visual interpretation and more accurate analysis of spatial space. The point cloud can be marked with GPS data that allows for accurate time-referencing and temporal synchronization. This is helpful for quality control, and time-sensitive analysis.

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

Range Measurement Sensor

The core of LiDAR devices is a range sensor that emits a laser signal towards objects and surfaces. This pulse is reflected, and the distance can be determined by measuring the time it takes for the laser pulse to be able to reach the object's surface and then return to the sensor. Sensors are placed on rotating platforms to enable rapid 360-degree sweeps. These two dimensional data sets give a clear view of the robot's surroundings.

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

Range data is used to create two-dimensional contour maps of the area of operation. It can be combined with other sensors such as cameras or vision system to improve the performance and durability.

Cameras can provide additional information in visual terms to aid in the interpretation of range data and increase the accuracy of navigation. Certain vision systems are designed to use range data as an input to a computer generated model of the surrounding environment which can be used to guide the robot vacuums with lidar by interpreting what it sees.

It is important to know how a LiDAR sensor operates and what it is able to do. The robot can move between two rows of crops and the objective is to determine the right one by using the LiDAR data.

A technique called simultaneous localization and mapping (SLAM) can be employed to achieve this. SLAM is an iterative algorithm which makes use of the combination of existing circumstances, such as the robot's current position and orientation, as well as modeled predictions that are based on the current speed and direction sensors, and estimates of noise and error quantities, and iteratively approximates the solution to determine the robot's location and position. With 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 key role in a robot's capability to map its surroundings and locate itself within it. The evolution of the algorithm is a key research area for robotics and artificial intelligence. This paper surveys a variety of leading approaches to solving the SLAM problem and discusses the challenges that remain.

The main goal of SLAM is to estimate the robot's sequential movement in its surroundings while creating a 3D map of that environment. SLAM algorithms are based on characteristics extracted from sensor data, which can be either laser or camera data. These characteristics are defined as points of interest that can be distinguished from other features. These features could be as simple or complicated as a plane or corner.

The majority of Lidar sensors have a limited 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 a larger area of the surrounding area. This could lead to an improved navigation accuracy and a complete mapping of the surrounding.

To accurately estimate 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 a myriad of algorithms that can be used for this purpose, including iterative closest point and normal distributions transform (NDT) methods. These algorithms can be combined with sensor data to produce an 3D map that can be displayed as an occupancy grid or 3D point cloud.

A SLAM system may be complicated and require a significant amount of processing power in order to function efficiently. This is a problem for robotic systems that require to run in real-time or run on a limited hardware platform. To overcome these challenges, an SLAM system can be optimized to the specific sensor software and hardware. For example a laser scanner with high resolution and a wide FoV could require more processing resources than a cheaper, lower-resolution scanner.

Map Building

A map is an illustration of the surroundings, typically in three dimensions, that serves a variety of purposes. It can be descriptive (showing the precise location of geographical features to be used in a variety applications like a street map) as well as exploratory (looking for patterns and connections between phenomena and their properties, to look for deeper meaning in a specific topic, as with many thematic maps), or even explanatory (trying to communicate information about an object or process often using visuals, like graphs or illustrations).

Local mapping builds a 2D map of the surrounding area with the help of LiDAR sensors placed at the bottom of a Vacuum robot with Lidar, just above the ground level. This is accomplished by the sensor providing distance information from the line of sight of each pixel of the two-dimensional rangefinder that allows topological modeling of the surrounding area. The most common segmentation and navigation algorithms are based on this data.

Scan matching is an algorithm that makes use of distance information to determine the position and orientation of the AMR for each point. This is accomplished by minimizing the differences between the robot's future state and its current one (position or rotation). Several techniques have been proposed to achieve scan matching. Iterative Closest Point is the most popular technique, and has been tweaked several times over the years.

Scan-toScan Matching is yet 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 match its current surroundings due to changes. This approach is susceptible to a long-term shift in the map, since the accumulated corrections to position and pose are susceptible to inaccurate updating over time.

A multi-sensor Fusion system is a reliable solution that uses 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 environments that are constantly changing.