What Is Lidar Robot Navigation And Why Is Everyone Speakin' About It?
LiDAR Robot Navigation
LiDAR robot navigation is a complex combination of localization, mapping and path planning. This article will present these concepts and explain how they interact using an example of a robot achieving its goal in a row of crop.
LiDAR sensors have low power requirements, which allows them to increase a robot's battery life and reduce the need for raw data for localization algorithms. This allows for more iterations of SLAM without overheating GPU.
LiDAR Sensors
The central component of lidar systems is their sensor that emits laser light in the surrounding. The light waves bounce off objects around them at different angles based on their composition. The sensor records the amount of time required for each return and then uses it to calculate distances. The sensor is usually placed on a rotating platform permitting it to scan the entire surrounding area at high speed (up to 10000 samples per second).
LiDAR sensors can be classified based on the type of sensor they're designed for, whether applications in the air or on land. Airborne lidars are usually mounted on helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR is typically installed on a robotic platform that is stationary.
To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is typically captured through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems in order to determine the precise location of the sensor in the space and time. The information gathered is used to create a 3D representation of the surrounding.
LiDAR scanners can also be used to detect different types of surface, which is particularly useful when mapping environments that have dense vegetation. For instance, when a pulse passes through a forest canopy, it is common for it to register multiple returns. The first return is usually attributable to the tops of the trees, while the second is associated with the surface of the ground. If the sensor records these pulses in a separate way, it is called discrete-return LiDAR.
The Discrete Return scans can be used to study the structure of surfaces. For instance, a forest region could produce a sequence of 1st, 2nd, and 3rd returns, with a final, large pulse representing the ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of detailed terrain models.
Once an 3D map of the surrounding area has been built and the robot is able to navigate based on this data. This involves localization as well as building a path that will reach a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the original map and then updates the plan of travel in line with the new obstacles.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an outline of its surroundings and then determine the position of the robot relative to the map. Engineers utilize the information to perform a variety of purposes, including the planning of routes and obstacle detection.
To utilize SLAM your robot has to have a sensor that gives range data (e.g. A computer with the appropriate software for processing the data and cameras or lasers are required. You will also need an IMU to provide basic positioning information. The result is a system that will accurately determine the location of your robot in an unknown environment.
The SLAM system is complex and offers a myriad of back-end options. No matter which solution you choose to implement an effective SLAM, it requires constant communication between the range measurement device and the software that extracts the data and the vehicle or robot. It is a dynamic process that is almost indestructible.
As the robot moves and around, it adds new scans to its map. The SLAM algorithm compares these scans with the previous ones making use of a process known as scan matching. This assists in establishing loop closures. The SLAM algorithm is updated with its estimated robot trajectory once loop closures are detected.
The fact that the surroundings can change over time is a further factor that complicates SLAM. For instance, if a robot walks through an empty aisle at one point and then comes across pallets at the next spot, it will have difficulty finding these two points on its map. This is where handling dynamics becomes crucial, and this is a typical characteristic of the modern Lidar SLAM algorithms.
Despite these difficulties, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially beneficial in environments that don't allow the robot to rely on GNSS-based position, such as an indoor factory floor. It is important to keep in mind that even a well-designed SLAM system may experience errors. To correct these errors, it is important to be able detect them and comprehend their impact on the SLAM process.
Mapping
The mapping function creates a map of the robot's environment. This includes the robot and its wheels, actuators, and everything else within its field of vision. This map is used to perform localization, path planning and obstacle detection. This is a domain where 3D Lidars are especially helpful, since they can be treated as a 3D Camera (with a single scanning plane).
Map creation can be a lengthy process, but it pays off in the end. The ability to create an accurate and complete map of a robot's environment allows it to navigate with high precision, and also around obstacles.
As a general rule of thumb, the greater resolution the sensor, the more accurate the map will be. However, not all robots need high-resolution maps. For example, a floor sweeper may not need the same level of detail as an industrial robot that is navigating factories with huge facilities.
There are a variety of mapping algorithms that can be utilized with LiDAR sensors. One popular algorithm is called Cartographer, which uses the two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is particularly efficient when combined with odometry data.
GraphSLAM is a different option, that uses a set linear equations to represent constraints in a diagram. The constraints are represented by an O matrix, and an the X-vector. Each vertice of the O matrix is a distance from a landmark on X-vector. A GraphSLAM update is the addition and subtraction operations on these matrix elements which means that all of the O and X vectors are updated to accommodate new observations of the robot.
SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current position, but also the uncertainty of the features mapped by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location, and also to update the map.
Obstacle Detection
A robot must be able to sense its surroundings in order to avoid obstacles and reach its final point. It uses sensors such as digital cameras, infrared scans sonar, laser radar and others to determine the surrounding. It also uses inertial sensors to monitor its position, speed and its orientation. These sensors help it navigate in a safe manner and avoid collisions.
A range sensor is used to measure the distance between the robot and the obstacle. The sensor can be mounted on the robot, inside a vehicle or on poles. It is important to keep in mind that the sensor may be affected by many elements, including wind, rain, and fog. Therefore, it is crucial to calibrate the sensor before every use.
A crucial step in obstacle detection is to identify static obstacles. This can be accomplished using the results of the eight-neighbor cell clustering algorithm. This method isn't very accurate because of the occlusion induced by the distance between the laser lines and the camera's angular velocity. To overcome this problem multi-frame fusion was implemented to increase the accuracy of the static obstacle detection.
The method of combining roadside camera-based obstruction detection with a vehicle camera has shown to improve the efficiency of processing data. It also allows the possibility of redundancy for other navigational operations, like planning a path. This method provides a high-quality, reliable image of the environment. The method has been compared with other obstacle detection methods like YOLOv5, VIDAR, and monocular ranging in outdoor comparison experiments.
The results of the experiment revealed that the algorithm was able to accurately identify the height and position of obstacles as well as its tilt and rotation. It was also able to identify the size and color of the object. lidar robot vacuums showed excellent stability and durability, even in the presence of moving obstacles.