15 Amazing Facts About Lidar Robot Navigation That You Didn't Know About
LiDAR Robot Navigation
LiDAR robots navigate by using a combination of localization and mapping, as well as path planning. This article will present these concepts and show how they interact using an example of a robot reaching a goal in the middle of a row of crops.
LiDAR sensors are low-power devices that prolong the life of batteries on a robot and reduce the amount of raw data required to run localization algorithms. This allows for more variations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The central component of lidar systems is its sensor that emits laser light pulses into the environment. The light waves hit objects around and bounce back to the sensor at various angles, based on the composition of the object. The sensor is able to measure the amount of time it takes for each return, which is then used to calculate distances. The sensor is typically mounted on a rotating platform, permitting it to scan the entire area at high speeds (up to 10000 samples per second).
LiDAR sensors can be classified based on whether they're intended for applications in the air or on land. Airborne lidars are usually mounted on helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is typically installed on a robot platform that is stationary.
To accurately measure distances, the sensor must always know the exact location of the robot. This information is captured by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by LiDAR systems to calculate the precise location of the sensor in the space and time. This information is used to build a 3D model of the surrounding environment.
LiDAR scanners are also able to identify various types of surfaces which is especially useful when mapping environments that have dense vegetation. When a pulse passes a forest canopy, it is likely to register multiple returns. Usually, the first return is attributable to the top of the trees, while the final return is associated with the ground surface. If the sensor can record each pulse as distinct, it is called discrete return LiDAR.
Distinte return scans can be used to study the structure of surfaces. For instance, a forest region could produce the sequence of 1st 2nd and 3rd return, with a last large pulse representing the bare ground. The ability to divide these returns and save them as a point cloud makes it possible for the creation of precise terrain models.
Once a 3D model of the environment is created, the robot can begin to navigate using this information. This involves localization and building a path that will reach a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that are not listed in the map's original version and then updates the plan of travel according to the new obstacles.
cheapest robot vacuum with lidar (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 in relation to the map. Engineers utilize this information for a variety of tasks, including planning routes and obstacle detection.
To enable SLAM to function, your robot must have a sensor (e.g. a camera or laser) and a computer running the right software to process the data. You will also require an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that will accurately determine the location of your robot in an unknown environment.
The SLAM process is complex and a variety of back-end solutions exist. Regardless of which solution you choose for your SLAM system, a successful SLAM system requires a constant interaction between the range measurement device and the software that collects the data, and the vehicle or robot. This is a dynamic procedure with almost infinite variability.
As the robot moves the area, it adds new scans to its map. The SLAM algorithm will then compare these scans to the previous ones using a method called scan matching. This allows loop closures to be created. The SLAM algorithm is updated with its estimated robot trajectory once loop closures are detected.
Another factor that complicates SLAM is the fact that the environment changes over time. For example, if your robot is walking down an empty aisle at one point and then comes across pallets at the next location, it will have difficulty connecting these two points in its map. This is when handling dynamics becomes crucial and is a standard feature of modern Lidar SLAM algorithms.
Despite these challenges, a properly configured SLAM system can be extremely effective for navigation and 3D scanning. It is particularly useful in environments that don't allow the robot to rely on GNSS-based positioning, like an indoor factory floor. However, it is important to note that even a properly configured SLAM system can experience errors. It is vital to be able to spot these errors and understand how they impact the SLAM process in order to rectify them.
Mapping
The mapping function creates a map for a robot's environment. This includes the robot, its wheels, actuators and everything else that falls within its vision field. This map is used to perform the localization, planning of paths and obstacle detection. This is an area where 3D lidars are extremely helpful since they can be used like the equivalent of a 3D camera (with a single scan plane).
The map building process may take a while however the results pay off. The ability to build a complete and coherent map of the environment around a robot allows it to navigate with high precision, and also around obstacles.
As a rule of thumb, the greater resolution of the sensor, the more accurate the map will be. Not all robots require high-resolution maps. For instance, a floor sweeping robot may not require the same level of detail as an industrial robotics system operating in large factories.
This is why there are many different mapping algorithms for use with LiDAR sensors. Cartographer is a very popular algorithm that employs a two-phase pose graph optimization technique. It adjusts for drift while maintaining an accurate global map. It is especially useful when combined with the odometry.
Another alternative is GraphSLAM, which uses linear equations to model the constraints of graph. The constraints are represented by an O matrix, and a the X-vector. Each vertice of the O matrix is an approximate distance from a landmark on X-vector. A GraphSLAM update consists of an array of additions and subtraction operations on these matrix elements and the result is that all of the O and X vectors are updated to reflect new information about the robot.
Another efficient mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current location, but also the uncertainty of the features that were drawn by the sensor. The mapping function is able to make use of this information to better estimate its own location, allowing it to update the underlying map.
Obstacle Detection
A robot must be able to sense its surroundings to avoid obstacles and get to its desired point. It uses sensors like digital cameras, infrared scanners sonar and laser radar to sense its surroundings. Additionally, it employs inertial sensors to determine its speed and position, as well as its orientation. These sensors aid in navigation in a safe and secure manner and prevent collisions.
One important part of this process is obstacle detection, which involves the use of sensors to measure the distance between the robot and obstacles. The sensor can be attached to the vehicle, the robot, or a pole. It is crucial to keep in mind that the sensor may be affected by various factors, such as rain, wind, and fog. It is crucial to calibrate the sensors prior to each use.
The most important aspect of obstacle detection is the identification of static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However, this method is not very effective in detecting obstacles because of the occlusion caused by the distance between the different laser lines and the speed of the camera's angular velocity, which makes it difficult to identify static obstacles in one frame. To overcome this issue multi-frame fusion was implemented to improve the accuracy of the static obstacle detection.
The method of combining roadside camera-based obstruction detection with a vehicle camera has shown to improve data processing efficiency. It also provides redundancy for other navigation operations such as path planning. This method provides an image of high-quality and reliable of the environment. The method has been tested with other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.
The experiment results showed that the algorithm could correctly identify the height and location of an obstacle, as well as its tilt and rotation. It also had a good ability to determine the size of an obstacle and its color. The method was also reliable and steady even when obstacles moved.