LiDAR Robot Navigation
LiDAR robots navigate by using a combination of localization and mapping, as well as path planning. This article will explain the concepts and explain how they function using an easy example where the robot reaches the desired goal within a row of plants.
LiDAR sensors have low power requirements, allowing them to prolong the life of a robot's battery and reduce the need for raw data for localization algorithms. This allows for more iterations of SLAM without overheating the GPU.
LiDAR Sensors
The sensor is the core of the Lidar system. It releases laser pulses into the surrounding. These pulses bounce off objects around them in different angles, based on their composition. The sensor records the time it takes for each return, which is then used to calculate distances. Sensors are positioned on rotating platforms, which allow them to scan the area around them quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified according to their intended applications on land or in the air. Airborne lidars are typically connected to helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR is usually mounted on a stationary robot platform.
To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually captured by an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems to calculate the exact location of the sensor in space and time. The information gathered is used to build a 3D model of the environment.
LiDAR scanners can also identify different kinds of surfaces, which is particularly useful when mapping environments with dense vegetation. For instance, when a pulse passes through a forest canopy it will typically register several returns. The first return is usually associated with the tops of the trees while the second one is attributed to the surface of the ground. If the sensor records each pulse as distinct, it is known as discrete return LiDAR.
Distinte return scanning can be useful for analysing surface structure. For instance the forest may result in one or two 1st and 2nd returns with the final large pulse representing bare ground. The ability to separate and store these returns in a point-cloud allows for detailed terrain models.
Once an 3D model of the environment is created, the robot will be equipped to navigate. This involves localization as well as making a path that will reach a navigation "goal." It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that are not present in the original map, and then updating the plan accordingly.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an image of its surroundings and then determine the location of its position relative to the map. Engineers utilize this information to perform a variety of tasks, such as planning routes and obstacle detection.
To be able to use SLAM your robot has to be equipped with a sensor that can provide range data (e.g. A computer with the appropriate software to process the data, as well as a camera or a laser are required. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The system will be able to track your robot's exact location in a hazy environment.
The SLAM process is complex, and many different back-end solutions are available. Whatever option you choose to implement a successful SLAM, it requires a constant interaction between the range measurement device and the software that extracts the data, as well as the vehicle or robot. This is a highly dynamic process that has an almost endless amount of variance.
As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans to earlier ones using a process called scan matching. This allows loop closures to be created. lidar robot vacuum and mop adjusts its robot's estimated trajectory when a loop closure has been identified.
The fact that the surroundings changes over time is another factor that complicates SLAM. For instance, if your robot is walking down an empty aisle at one point and then encounters stacks of pallets at the next location it will have a difficult time connecting these two points in its map. This is where handling dynamics becomes important and is a standard characteristic of the modern Lidar SLAM algorithms.
SLAM systems are extremely effective at navigation and 3D scanning despite these challenges. It is especially beneficial in environments that don't let the robot rely on GNSS-based positioning, such as an indoor factory floor. It is important to keep in mind that even a properly configured SLAM system may have errors. To correct these errors it is crucial to be able detect them and understand their impact on the SLAM process.
Mapping
The mapping function builds a map of the robot's surroundings which includes the robot including its wheels and actuators, and everything else in its field of view. This map is used for localization, path planning, and obstacle detection. This is an area where 3D lidars can be extremely useful because they can be utilized as an actual 3D camera (with a single scan plane).
Map building can be a lengthy process but it pays off in the end. The ability to build a complete, coherent map of the robot's surroundings allows it to perform high-precision navigation, as well as navigate around obstacles.
The greater the resolution of the sensor, the more precise will be the map. However it is not necessary for all robots to have high-resolution maps: for example, a floor sweeper may not need the same degree of detail as an industrial robot navigating factories with huge facilities.
There are a variety of mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which employs a two-phase pose graph optimization technique to correct for drift and maintain an accurate global map. It is especially useful when combined with Odometry.
Another alternative is GraphSLAM that employs linear equations to model constraints in graph. The constraints are modelled as an O matrix and a X vector, with each vertice of the O matrix containing the distance to a landmark on the X vector. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, with the end result being that all of the O and X vectors are updated to accommodate new robot observations.
Another useful mapping algorithm is SLAM+, which combines the use of odometry with 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 mapped by the sensor. The mapping function can then utilize this information to improve its own position, allowing it to update the base map.
Obstacle Detection
A robot should be able to see its surroundings to avoid obstacles and reach its destination. It makes use of sensors like digital cameras, infrared scans sonar and laser radar to sense the surroundings. Additionally, it utilizes inertial sensors that measure its speed and position, as well as its orientation. These sensors help it navigate safely and avoid collisions.
A range sensor is used to measure the distance between the robot and the obstacle. The sensor can be placed on the robot, inside a vehicle or on the pole. It is important to remember that the sensor could be affected by a variety of factors, such as wind, rain, and fog. Therefore, it is essential to calibrate the sensor prior to each use.
The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. However, this method has a low accuracy in detecting due to the occlusion created by the spacing between different laser lines and the angle of the camera making it difficult to detect static obstacles within a single frame. To overcome this problem, a method called multi-frame fusion has been used to increase the accuracy of detection of static obstacles.
The technique of combining roadside camera-based obstacle detection with vehicle camera has been proven to increase data processing efficiency. It also provides the possibility of redundancy for other navigational operations such as planning a path. The result of this technique is a high-quality image of the surrounding area that is more reliable than one frame. In outdoor tests the method was compared against other methods for detecting obstacles like YOLOv5 monocular ranging, and VIDAR.
The results of the test showed that the algorithm could accurately identify the height and position of an obstacle, as well as its tilt and rotation. It was also able detect the color and size of the object. The method also exhibited good stability and robustness even when faced with moving obstacles.