See What Lidar Robot Navigation Tricks The Celebs Are Utilizing
LiDAR Robot Navigation
LiDAR robot navigation is a sophisticated combination of localization, mapping and path planning. This article will introduce these concepts and explain how they function together with an example of a robot achieving a goal within the middle of a row of crops.
LiDAR sensors are low-power devices which can prolong the battery life of a robot and reduce the amount of raw data required for localization algorithms. This allows for a greater number of iterations of SLAM without overheating GPU.
LiDAR Sensors
The heart of lidar systems is their sensor that emits pulsed laser light into the environment. These light pulses bounce off surrounding objects at different angles depending on their composition. The sensor measures the amount of time it takes for each return and then uses it to determine distances. Sensors are positioned on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second).
lidar robot vacuum sensors are classified based on their intended applications in the air or on land. Airborne lidars are often attached to helicopters or unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform.
To accurately measure distances, the sensor must be aware of the precise location of the robot at all times. This information is gathered by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by LiDAR systems in order to determine the precise position of the sensor within space and time. This information is used to build a 3D model of the surrounding environment.
lidar vacuum scanners can also detect different kinds of surfaces, which is particularly useful when mapping environments with dense vegetation. For example, when a pulse passes through a canopy of trees, it is likely to register multiple returns. The first one is typically attributed to the tops of the trees while the second is associated with the ground's surface. If the sensor can record each pulse as distinct, it is known as discrete return LiDAR.
Discrete return scans can be used to analyze the structure of surfaces. For example the forest may produce an array of 1st and 2nd returns, with the final big pulse representing bare ground. The ability to separate and record these returns as a point cloud allows for detailed models of terrain.
Once an 3D map of the surrounding area is created and the robot is able to navigate based on this data. This involves localization and creating a path to take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the process that detects new obstacles that were not present in the map's original version and adjusts the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its environment, and then determine its position in relation to the map. Engineers use this information for a range of tasks, such as planning routes and obstacle detection.
To enable SLAM to function, your robot must have sensors (e.g. A computer with the appropriate software to process the data and either a camera or laser are required. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that will precisely track the position of your robot in an unspecified environment.
The SLAM system is complicated and there are many different back-end options. No matter which one you choose the most effective SLAM system requires a constant interaction between the range measurement device and the software that collects the data and the robot or vehicle itself. This is a highly dynamic procedure that can have an almost infinite amount of variability.
As the robot moves and around, it adds new scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method called scan matching. This assists in establishing loop closures. When a loop closure is discovered when loop closure is detected, the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.
Another issue that can hinder SLAM is the fact that the surrounding changes over time. For instance, if your robot is walking down an aisle that is empty at one point, but it comes across a stack of pallets at a different location it might have trouble finding the two points on its map. Dynamic handling is crucial in this scenario, and they are a part of a lot of modern Lidar SLAM algorithm.
SLAM systems are extremely efficient in 3D scanning and navigation despite these limitations. It is especially beneficial in situations where the robot vacuums with obstacle avoidance lidar can't rely on GNSS for its positioning for example, an indoor factory floor. It's important to remember that even a properly-configured SLAM system can be prone to mistakes. It is vital to be able to detect these flaws and understand how they affect the SLAM process in order to correct them.
Mapping
The mapping function creates a map of a robot's surroundings. This includes the robot and its wheels, actuators, and everything else that is within its vision field. The map is used for localization, route planning and obstacle detection. This is an area where 3D Lidars are especially helpful as they can be treated as an 3D Camera (with one scanning plane).
The process of creating maps can take some time however, the end result pays off. The ability to create a complete and coherent map of a robot's environment allows it to move with high precision, and also over obstacles.
As a rule of thumb, the higher resolution the sensor, the more accurate the map will be. Not all robots require high-resolution maps. For example, a floor sweeping robot might not require the same level detail as a robotic system for industrial use navigating large factories.
There are a variety of mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a popular algorithm that utilizes a two-phase pose graph optimization technique. It corrects for drift while maintaining an unchanging global map. It is particularly useful when paired with the odometry information.
GraphSLAM is another option, which utilizes a set of linear equations to represent constraints in a diagram. The constraints are represented by an O matrix, and a X-vector. Each vertice in the O matrix represents the distance to a landmark on 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 observations of the robot.
Another useful mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF changes the uncertainty of the robot's position as well as the uncertainty of the features that were mapped by the sensor. The mapping function is able to utilize this information to estimate its own position, which allows it to update the base map.
Obstacle Detection
A robot needs to be able to perceive its environment so that it can overcome obstacles and reach its goal. It employs sensors such as digital cameras, infrared scans sonar, laser radar and others to sense the surroundings. Additionally, it employs inertial sensors to determine its speed and position as well as its orientation. These sensors allow it to navigate in a safe manner and avoid collisions.
A range sensor is used to gauge the distance between the robot and the obstacle. The sensor can be mounted to the vehicle, the robot or even a pole. It is important to keep in mind that the sensor could be affected by many elements, including rain, wind, or fog. Therefore, it is important to calibrate the sensor before each use.
The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However, this method has a low detection accuracy due to the occlusion caused by the gap between the laser lines and the speed of the camera's angular velocity making it difficult to recognize static obstacles in one frame. To overcome this problem, a method of multi-frame fusion has been used to increase the accuracy of detection of static obstacles.
The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to increase the efficiency of processing data and reserve redundancy for further navigational operations, like path planning. The result of this technique is a high-quality picture of the surrounding area that is more reliable than one frame. The method has been tested against other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging, in outdoor comparative tests.
The results of the test proved that the algorithm could correctly identify the height and location of an obstacle as well as its tilt and rotation. It also had a great ability to determine the size of the obstacle and its color. The algorithm was also durable and steady, even when obstacles moved.