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lidar explained Robot Navigation

LiDAR robots navigate by using a combination of localization, mapping, and also path planning. This article will outline the concepts and show how they work by using an easy example where the robot reaches the desired goal within a row of plants.

LiDAR sensors have low power demands allowing them to extend 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 the GPU.

lidar navigation Sensors

The sensor is at the center of lidar robotic navigation systems. It emits laser pulses into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor monitors the time it takes for each pulse to return and then uses that information to determine distances. The sensor is usually placed on a rotating platform permitting it to scan the entire area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified based on their intended applications on land or in the air. Airborne lidars are typically connected to helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually 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 usually gathered by an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to determine the precise position of the sensor within space and time. This information is used to create a 3D representation of the surrounding environment.

LiDAR scanners are also able to identify different surface types which is especially beneficial for mapping environments with dense vegetation. When a pulse crosses a forest canopy, it is likely to generate multiple returns. The first return is usually attributed to the tops of the trees, while the last is attributed with the ground's surface. If the sensor records each peak of these pulses as distinct, this is known as discrete return lidar robot vacuum cleaner.

The Discrete Return scans can be used to analyze the structure of surfaces. For instance, a forest region could produce a sequence of 1st, 2nd and 3rd return, with a final, large pulse that represents the ground. The ability to separate and record these returns in a point-cloud permits detailed terrain models.

Once a 3D model of the surrounding area has been created and the robot has begun to navigate using this information. This involves localization, constructing a path to get to a destination,' and dynamic obstacle detection. The latter is the process of identifying obstacles that are not present in the original map, and then updating the plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its environment and then identify its location relative to that map. Engineers make use of this information to perform a variety of tasks, such as planning a path and identifying obstacles.

To utilize SLAM, your robot needs to be equipped with a sensor that can provide range data (e.g. A computer that has the right software to process the data and cameras or lasers are required. You'll also require an IMU to provide basic positioning information. The system can track your robot's location accurately in an unknown environment.

The SLAM system is complicated and offers a myriad of back-end options. Whatever solution you choose, a successful SLAM system requires a constant interplay between the range measurement device, the software that extracts the data, and the vehicle or robot itself. This is a highly dynamic process that can have an almost endless amount of variance.

When the robot moves, it adds 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 adjusts its estimated robot trajectory once loop closures are detected.

Another factor that complicates SLAM is the fact that the environment changes over time. For instance, if a robot travels through an empty aisle at one point, and is then confronted by pallets at the next point, it will have difficulty finding these two points on its map. This is where handling dynamics becomes important, and this is a common characteristic of the modern Lidar SLAM algorithms.

Despite these challenges, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments where the robot can't rely on GNSS for its positioning, such as an indoor factory floor. However, it is important to note that even a properly configured SLAM system can experience mistakes. It is crucial 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 surroundings. This includes the robot and its wheels, actuators, and everything else that falls within its vision field. This map is used for localization, path planning, and obstacle detection. This is a field where 3D Lidars can be extremely useful as they can be treated as an 3D Camera (with a single scanning plane).

The process of creating maps may take a while however the results pay off. The ability to create an accurate, complete map of the surrounding area allows it to conduct high-precision navigation, as as navigate around obstacles.

As a rule of thumb, the higher resolution the sensor, more precise the map will be. However it is not necessary for all robots to have high-resolution maps: for example floor sweepers might not need the same level of detail as an industrial robot that is navigating large factory facilities.

To this end, there are a number of different mapping algorithms that can be used with LiDAR sensors. Cartographer is a popular algorithm that employs a two-phase pose graph optimization technique. It adjusts for drift while maintaining a consistent global map. It is particularly useful when paired with Odometry.

Another option is GraphSLAM which employs a system of linear equations to model the constraints in a graph. The constraints are represented by an O matrix, as well as an vector X. Each vertice in the O matrix is an approximate distance from the X-vector's landmark. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all 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 mapping and odometry using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty of the robot's current location, but also the uncertainty in the features that have been drawn by the sensor. This information can be used by the mapping function to improve its own estimation of its location, and also to update the map.

Obstacle Detection

A robot must be able to sense its surroundings to avoid obstacles and reach its goal point. It uses sensors like digital cameras, infrared scanners sonar and laser radar to determine its surroundings. It also uses inertial sensors to monitor its speed, position and the direction. These sensors enable it to navigate in a safe manner and avoid collisions.

A range sensor is used to gauge the distance between a robot and an obstacle. The sensor can be mounted to the vehicle, the robot, or a pole. It is crucial to remember that the sensor is affected by a variety of factors like rain, wind and fog. It is important to calibrate the sensors prior to each use.

The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. 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 angular velocity of the camera, which makes it difficult to detect static obstacles in a single frame. To solve this issue, a technique of multi-frame fusion was developed to increase the accuracy of detection of static obstacles.

The method of combining roadside unit-based as well as obstacle detection by a vehicle camera has been proven to improve the efficiency of data processing 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 a single frame. In outdoor tests, the method was compared against other methods of obstacle detection like YOLOv5, monocular ranging and VIDAR.

The results of the experiment revealed that the algorithm was able to accurately identify the height and location of an obstacle, as well as its tilt and rotation. It was also able detect the color and size of an object. The method also showed good stability and robustness, even when faced with moving obstacles.