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LiDAR Robot Navigation
LiDAR robot navigation is a complex combination of localization, mapping, and path planning. This article will explain the concepts and explain how they work by using a simple example where the robot reaches an objective within the space of a row of plants.
LiDAR sensors have modest power demands allowing them to increase the life of a robot's battery and reduce the need for raw data for localization algorithms. This enables more variations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The sensor is at the center of a lidar robot vacuum system. It emits laser pulses into the environment. The light waves bounce off surrounding objects in different angles, based on their composition. The sensor measures how long it takes each pulse to return and then utilizes that information to calculate distances. Sensors are placed on rotating platforms that allow them to scan the surroundings quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified based on the type of sensor they are designed for airborne or terrestrial application. Airborne lidar systems are typically mounted on aircrafts, helicopters or UAVs. (UAVs). Terrestrial LiDAR systems are usually placed on a stationary robot platform.
To accurately measure distances the sensor must always know the exact location of the robot. This information is captured using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are utilized by LiDAR systems in order to determine the precise location of the sensor in the space and time. This information is then used to create a 3D model of the environment.
lidar sensor vacuum cleaner scanners can also detect different kinds of surfaces, which is especially useful when mapping environments with dense vegetation. When a pulse crosses a forest canopy it will usually generate multiple returns. Usually, the first return is attributable to the top of the trees, and the last one is related to the ground surface. If the sensor captures each peak of these pulses as distinct, it is referred to as discrete return LiDAR.
The Discrete Return scans can be used to study the structure of surfaces. For instance, a forested 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 allows for the creation of precise terrain models.
Once a 3D model of the environment is created and the robot is able to use this data to navigate. This process involves localization and creating a path to take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the process of identifying new obstacles that are not present in the original map, and adjusting the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then determine its location in relation to the map. Engineers utilize the information for a number of tasks, including planning a path and identifying obstacles.
To utilize SLAM, your robot needs to have a sensor that gives range data (e.g. A computer with the appropriate software for processing the data as well as cameras or lasers are required. You also need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that can accurately track the location of your robot in an unspecified environment.
The SLAM process is a complex one, and many different back-end solutions exist. Whatever solution you choose to implement the success of SLAM is that it requires constant communication between the range measurement device and the software that extracts data and also the robot or vehicle. This is a highly dynamic procedure that has an almost endless amount of variance.
As the robot moves around and around, it adds new scans to its map. The SLAM algorithm then compares these scans to earlier ones using a process known as scan matching. This allows loop closures to be created. When a loop closure is detected it is then the SLAM algorithm makes use of this information to update its estimate of the cheapest robot vacuum with lidar's trajectory.
Another factor that makes SLAM is the fact that the scene changes as time passes. For instance, if your robot is walking down an aisle that is empty at one point, and then encounters a stack of pallets at another point, it may have difficulty finding the two points on its map. This is where the handling of dynamics becomes critical, and this is a common characteristic of the modern Lidar SLAM algorithms.
SLAM systems are extremely efficient in 3D scanning and navigation despite these challenges. It is especially beneficial in environments that don't allow the robot to rely on GNSS positioning, such as an indoor factory floor. It is important to keep in mind that even a properly-configured SLAM system could be affected by errors. To correct these errors, it is important to be able detect the effects of these errors and their implications 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 that falls within its vision field. This map is used to perform localization, path planning, and obstacle detection. This is an area in which 3D lidars can be extremely useful since they can be effectively treated as the equivalent of a 3D camera (with only one scan plane).
The process of building maps may take a while, but the results pay off. The ability to create an accurate, complete map of the robot's environment allows it to perform high-precision navigation, as well being able to navigate around obstacles.
As a rule, the higher the resolution of the sensor the more precise will be the map. Not all robots require maps with high resolution. For example, a floor sweeping robot might not require the same level of detail as an industrial robotics system operating in large factories.
To this end, there are many different mapping algorithms for use with LiDAR sensors. Cartographer is a popular algorithm that utilizes a two-phase pose graph optimization technique. It corrects for drift while ensuring an accurate global map. It is particularly effective when used in conjunction with Odometry.
GraphSLAM is a different option, that uses a set linear equations to represent constraints in the form of a diagram. The constraints are represented as an O matrix, and an vector X. Each vertice of the O matrix represents a distance from the X-vector's landmark. A GraphSLAM Update is a sequence of additions and subtractions on these matrix elements. The end result is that all O and X Vectors are updated in order to take into account the latest observations made by the robot.
SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF changes the uncertainty of the robot's location as well as the uncertainty of the features that were recorded 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 to avoid obstacles and reach its final point. It uses sensors such as digital cameras, infrared scans, sonar and laser radar to sense the surroundings. Additionally, it employs inertial sensors that measure its speed and position as well as its orientation. These sensors allow it to navigate without danger and avoid collisions.
A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be mounted on the robot, in a vehicle or on a pole. It is important to remember that the sensor may be affected by various elements, including wind, rain, and fog. Therefore, it is crucial to calibrate the sensor before every use.
The most important aspect of obstacle detection is identifying static obstacles. This can be done by using the results of the eight-neighbor cell clustering algorithm. However this method has a low accuracy in detecting because of the occlusion caused by the spacing between different laser lines and the speed of the camera's angular velocity making it difficult to recognize static obstacles within a single frame. To address this issue, a method called multi-frame fusion has been used to increase the detection accuracy of static obstacles.
The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to increase the data processing efficiency and reserve redundancy for further navigation operations, such as path planning. The result of this method is a high-quality image of the surrounding environment that is more reliable than one frame. In outdoor comparison tests the method was compared against other methods for detecting obstacles like YOLOv5 monocular ranging, and VIDAR.
The results of the experiment revealed that the algorithm was able accurately determine the location and height of an obstacle, as well as its rotation and tilt. It also showed a high ability to determine the size of the obstacle and its color. The method was also robust and steady even when obstacles were moving.