LiDAR Robot NavigationLiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will explain the concepts and demonstrate how they work using a simple example where the robot reaches an objective within the space of a row of plants.
LiDAR sensors are low-power devices that extend the battery life of robots and decrease the amount of raw data needed for localization algorithms. This allows for more iterations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The central component of a lidar system is its sensor, which emits laser light in the environment. These pulses bounce off surrounding objects at different angles based on their composition. The sensor is able to measure the amount of time required for each return, which is then used to determine distances. Sensors are positioned on rotating platforms that allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).
LiDAR sensors can be classified based on whether they're designed for applications in the air or on land. Airborne lidars are often connected to helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR is typically installed on a robotic platform that is stationary.
To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is recorded using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use sensors to compute the exact location of the sensor in space and time, which is later used to construct a 3D map of the surrounding area.
LiDAR scanners can also be used to detect different types of surface and types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. When a pulse passes through a forest canopy it will usually register multiple returns. Usually, the first return is attributed to the top of the trees while the final return is attributed to the ground surface. If the sensor records each pulse as distinct, this is referred to as discrete return
lidar robot vacuum and mop.
The use of Discrete Return scanning can be helpful in analysing surface structure. For instance, a forested area could yield a sequence of 1st, 2nd, and 3rd returns, with a last large pulse that represents the ground. The ability to separate and store these returns in a point-cloud permits detailed terrain models.
Once a 3D model of the surroundings has been built, the robot can begin to navigate based on this data. This process involves localization and building a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This process 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
lidar robot Navigation localization) is an algorithm that allows your robot to map its surroundings, and then determine its position in relation to that map. Engineers make use of this information to perform a variety of tasks, including path planning and obstacle detection.
To utilize SLAM your robot has to have a sensor that provides range data (e.g. A computer that has the right software to process the data, as well as cameras or lasers are required. Also, you will require an IMU to provide basic information about your position. The result is a system that can accurately determine the location of your robot in an unspecified environment.
The SLAM system is complicated and there are a variety of back-end options. Whatever option you choose for the success of SLAM, it requires constant communication between the range measurement device and the software that extracts the data and also the robot or vehicle. This is a highly dynamic procedure that is prone to an unlimited amount of variation.
When the robot moves, it adds 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. If a loop closure is identified, the SLAM algorithm utilizes 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 instance, if your robot is walking along an aisle that is empty at one point, and then encounters a stack of pallets at a different point it may have trouble connecting the two points on its map. This is where handling dynamics becomes critical, and this is a common feature of modern Lidar SLAM algorithms.
SLAM systems are extremely effective at navigation and 3D scanning despite these limitations. It is particularly useful in environments that don't permit the robot to depend on GNSS for position, such as an indoor factory floor. However, it's important to remember that even a well-configured SLAM system can be prone to mistakes. To fix these issues, it is important to be able to recognize the effects of these errors and their implications on the SLAM process.
Mapping
The mapping function creates a map of the robot's environment that includes the robot itself, its wheels and actuators as well as everything else within the area of view. This map is used for localization, route planning and obstacle detection. This is an area where 3D lidars are particularly helpful, as they can be utilized as an actual 3D camera (with a single scan plane).
The process of creating maps may take a while, but the results pay off. The ability to create an accurate, complete map of the surrounding area allows it to perform high-precision navigation, as well being able to navigate around obstacles.
As a rule of thumb, the greater resolution the sensor, more accurate the map will be. Not all robots require maps with high resolution. For example a floor-sweeping robot may not require the same level of detail as a robotic system for industrial use that is navigating factories of a large size.
This is why there are many different mapping algorithms to use with LiDAR sensors. Cartographer is a well-known algorithm that uses a two-phase pose graph optimization technique. It corrects for drift while maintaining an accurate global map. It is particularly efficient when combined with Odometry data.
Another option is GraphSLAM that employs linear equations to represent the constraints of a graph. The constraints are modelled as an O matrix and a the X vector, with every vertex of the O matrix containing the distance to a landmark on the X vector. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all the O and X Vectors are updated in order to account for
LiDAR Robot Navigation the new observations made by the robot.
Another helpful mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty of the robot's current position but also the uncertainty of the features that were drawn 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 must be able to see its surroundings so it can avoid obstacles and get to its desired point. It makes use of sensors like digital cameras, infrared scans laser radar, and sonar to detect the environment. It also utilizes an inertial sensor to measure its speed, location and its orientation. These sensors aid in navigation in a safe manner and avoid collisions.
A range sensor is used to determine the distance between the robot and the obstacle. The sensor can be mounted to the vehicle, the robot or a pole. It is important to keep in mind that the sensor could be affected by many factors, such as rain, wind, and fog. It is important to calibrate the sensors prior to each use.