LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization and mapping, and also path planning. This article will outline the concepts and show how they function using an example in which the
robot vacuum obstacle avoidance lidar reaches a goal within a row of plants.
LiDAR sensors have modest 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 the SLAM algorithm without overheating the GPU.
lidar based robot vacuum Sensors
The sensor is the core of Lidar systems. It emits laser pulses into the environment. These light pulses strike objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor records the time it takes to return each time, which is then used to determine distances. The sensor is typically mounted on a rotating platform, which allows it to scan the entire area at high speed (up to 10000 samples per second).
LiDAR sensors are classified based on the type of sensor they are designed for airborne or terrestrial application. Airborne lidars are often mounted on helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR is typically installed on a stationary robot platform.
To accurately measure distances, the sensor needs to 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 employed by LiDAR systems to determine the exact location of the sensor within the space and time. This information is then used to build a 3D model of the environment.
LiDAR scanners can also be used to identify different surface types which is especially useful when mapping environments that have dense vegetation. For instance, if the pulse travels through a forest canopy it will typically register several returns. The first return is usually attributed to the tops of the trees while the second is associated with the surface of the ground. If the sensor captures these pulses separately, it is called discrete-return LiDAR.
Distinte return scanning can be useful in analyzing the structure of surfaces. For instance, a forest area could yield a sequence of 1st, 2nd and 3rd return, with a final large pulse representing the ground. The ability to separate and record these returns as a point-cloud permits detailed models of terrain.
Once a 3D map of the surroundings has been built and the robot is able to navigate using this information. This involves localization and building a path that will reach a navigation "goal." It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that aren't visible in the original map, and updating the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine the location of its position in relation to the map. Engineers make use of this information for a number of tasks, including path planning and obstacle identification.
To allow SLAM to work the robot needs an instrument (e.g. the laser or camera) and a computer that has the appropriate software to process the data. You'll also require an IMU to provide basic positioning information. The system will be able to track your robot's exact location in a hazy environment.
The SLAM process is extremely complex, and many different back-end solutions are available. Whatever option you choose to implement a successful SLAM it requires constant communication between the range measurement device and the software that extracts data, as well as the vehicle or robot. This is a dynamic procedure that is almost indestructible.
As the
robot vacuums with lidar moves about and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones making use of a process known as scan matching. This allows loop closures to be created. If a loop closure is discovered, the SLAM algorithm uses this information to update its estimated robot trajectory.
The fact that the surroundings changes over time is a further factor that can make it difficult to use SLAM. For example, if your robot is walking down an empty aisle at one point, and then encounters stacks of pallets at the next spot it will be unable to matching these two points in its map. The handling dynamics are crucial in this scenario and are a part of a lot of modern Lidar SLAM algorithm.
SLAM systems are extremely effective at navigation and 3D scanning despite these limitations. It is particularly useful in environments that do not permit the robot to depend on GNSS for position, such as an indoor factory floor. It is crucial to keep in mind that even a properly-configured SLAM system could be affected by errors. To correct these mistakes it is crucial to be able to spot them and understand their impact on the SLAM process.
Mapping
The mapping function creates a map of the robot's surrounding, which includes the robot itself as well as its wheels and actuators as well as everything else within the area of view. This map is used for the localization of the
robot vacuums with lidar, route planning and obstacle detection. This is an area in which 3D lidars are particularly helpful, as they can be effectively treated like 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 and consistent map of a robot's environment allows it to navigate with great precision, and also over obstacles.
As a rule, the greater the resolution of the sensor then the more precise will be the map. However there are exceptions to the requirement for high-resolution maps: for example floor sweepers may not require the same amount of detail as a industrial robot that navigates factories of immense size.
There are a variety of mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular algorithm that employs the two-phase pose graph optimization technique. It corrects for drift while ensuring an unchanging global map. It is particularly beneficial when used in conjunction with Odometry data.
Another option is GraphSLAM, which uses a system of linear equations to model the constraints of a graph. The constraints are modeled as an O matrix and a one-dimensional X vector, each vertice of the O matrix representing the distance to a point on the X vector. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The end result is that both the O and X Vectors are updated in order to account for the new observations made by the robot.
Another useful mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's position as well as the uncertainty of the features mapped by the sensor. This information can be used by the mapping function to improve its own estimation of its location and to update the map.
Obstacle Detection
A robot should be able to detect its surroundings to avoid obstacles and get to its goal. It employs sensors such as digital cameras, infrared scans, laser radar, and sonar to detect the environment. Additionally, it employs inertial sensors to determine its speed and position as well as its orientation. These sensors help it navigate in a safe and secure manner and prevent collisions.
A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be mounted on the robot, inside an automobile or on the pole. It is crucial to keep in mind that the sensor may be affected by many factors, such as wind, rain, and fog. Therefore, it is essential to calibrate the sensor prior every use.
The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method isn't particularly accurate because of the occlusion created by the distance between the laser lines and the camera's angular velocity. To overcome this issue multi-frame fusion was implemented to increase the accuracy of static obstacle detection.
The technique of combining roadside camera-based obstruction detection with the vehicle camera has proven to increase data processing efficiency. It also reserves the possibility of redundancy for other navigational operations like path planning. This method provides an image of high-quality and reliable of the environment. In outdoor comparison tests the method was compared with other methods for detecting obstacles like YOLOv5 monocular ranging, and VIDAR.
The experiment results proved that the algorithm could correctly identify the height and position of obstacles as well as its tilt and rotation. It also showed a high performance in detecting the size of obstacles and its color. The method also demonstrated good stability and robustness, even when faced with moving obstacles.
