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LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of localization, mapping and path planning. This article will introduce the concepts and demonstrate how they function using an easy example where the robot is able to reach an objective within a plant row.

LiDAR sensors are relatively low power demands allowing them to extend the battery life of a robot and reduce the raw data requirement for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.

LiDAR Sensors

The core of a lidar system is its sensor that emits pulsed laser light into the surrounding. These light pulses strike objects and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor determines how long it takes for each pulse to return and then uses that information to determine distances. Sensors are placed on rotating platforms, which allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).

lidar vacuum robot sensors are classified according to their intended applications on land or in the air. Airborne lidars are typically attached to helicopters or unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are typically mounted on a stationary robot platform.

To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is recorded by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems make use of these sensors to compute the exact location of the sensor in space and time, which is later used to construct an image of 3D of the surroundings.

LiDAR scanners can also identify different types of surfaces, which is especially useful when mapping environments with dense vegetation. For instance, if an incoming pulse is reflected through a forest canopy it will typically register several returns. The first one is typically attributed to the tops of the trees while the second is associated with the surface of the ground. If the sensor records each pulse as distinct, this is referred to as discrete return LiDAR.

The use of Discrete Return scanning can be helpful in analyzing the structure of surfaces. For instance, a forested region could produce an array of 1st, 2nd and 3rd return, with a last large pulse representing the bare ground. The ability to separate and store these returns as a point-cloud permits detailed models of terrain.

Once a 3D model of the environment is created the robot will be able to use this data to navigate. This process involves localization, creating the path needed to reach a navigation 'goal and dynamic obstacle detection. This is the method of identifying new obstacles that aren't present in the map originally, and then updating the plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an outline of its surroundings and then determine the position of the robot relative to the map. Engineers make use of this information for a range of tasks, including path planning and obstacle detection.

To be able to use SLAM, your robot needs to have a sensor that provides range data (e.g. A computer with the appropriate software to process the data as well as either a camera or laser are required. You also need an inertial measurement unit (IMU) to provide basic information on your location. The system will be able to track the precise location of your robot in a hazy 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 constant interaction between the range measurement device and the software that collects the data, and the vehicle or robot itself. This is a dynamic procedure that is almost indestructible.

As the robot moves around, it adds new scans to its map. The SLAM algorithm analyzes these scans against prior ones using a process known as scan matching. This helps to establish loop closures. If a loop closure is discovered, the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.

Another factor that complicates SLAM is the fact that the surrounding changes over time. For example, if your robot is walking through an empty aisle at one point and is then confronted by pallets at the next point, it will have difficulty matching these two points in its map. This is when handling dynamics becomes critical and is a common characteristic of modern Lidar SLAM algorithms.

Despite these challenges, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is particularly useful in environments where the robot isn't able to rely on GNSS for its positioning, such as an indoor factory floor. It's important to remember that even a well-designed SLAM system could be affected by mistakes. It is crucial to be able recognize these issues and comprehend how they impact the SLAM process in order to fix them.

Mapping

The mapping function builds an image of the robot vacuum lidar's environment which includes the robot itself including its wheels and actuators, and everything else in its view. This map is used for the localization, planning of paths and obstacle detection. This is an area in which 3D lidars are particularly helpful since they can be used like a 3D camera (with one scan plane).

Map creation is a long-winded process but it pays off in the end. The ability to create an accurate, complete map of the surrounding area allows it to perform high-precision navigation as well as navigate around obstacles.

The greater the resolution of the sensor, the more precise will be the map. Not all robots require high-resolution maps. For instance, LiDAR Robot Navigation a floor sweeping robot may not require the same level detail as an industrial robotics system navigating large factories.

To this end, there are many different mapping algorithms to use with LiDAR sensors. Cartographer is a well-known algorithm that utilizes a two phase pose graph optimization technique. It corrects for drift while ensuring a consistent global map. It is especially useful when combined with Odometry.

GraphSLAM is another option, which utilizes a set of linear equations to represent constraints in the form of a diagram. The constraints are represented by an O matrix, and an vector X. Each vertice in the O matrix represents an approximate distance from an X-vector landmark. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements, with the end result being that all of the O and X vectors are updated to account for new observations of the robot.

SLAM+ is another useful mapping algorithm that combines odometry with mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty of the robot's current position but also the uncertainty in the features recorded by the sensor. The mapping function will make use of this information to improve its own position, which allows it to update the underlying map.

Obstacle Detection

A robot must be able to see its surroundings in order to avoid obstacles and reach its goal point. It employs sensors such as digital cameras, infrared scans, sonar, laser radar and others to sense the surroundings. It also makes use of an inertial sensors to monitor its speed, location and its orientation. These sensors help it navigate in a safe and secure manner and prevent collisions.

A key element of this process is the detection of obstacles that consists of the use of sensors to measure the distance between the robot and the obstacles. The sensor can be mounted to the vehicle, the robot or even a pole. It is important to remember that the sensor may be affected by many elements, including rain, wind, and fog. It is crucial to calibrate the sensors prior every use.

An important step in obstacle detection is identifying static obstacles. This can be accomplished by using the results of the eight-neighbor-cell clustering algorithm. However this method has a low detection accuracy due to the occlusion created by the gap between the laser lines and the angle of the camera, which makes it difficult to recognize static obstacles in a single frame. To address this issue, a method of multi-frame fusion has been employed to increase the accuracy of detection of static obstacles.

The method of combining roadside unit-based as well as vehicle camera obstacle detection has been proven to increase the data processing efficiency and reserve redundancy for subsequent navigational operations, like path planning. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. The method has been tested with other obstacle detection techniques including YOLOv5 VIDAR, YOLOv5, as well as monocular ranging in outdoor tests of comparison.

The results of the experiment proved that the algorithm could accurately determine the height and location of obstacles as well as its tilt and rotation. It also had a good performance in identifying the size of an obstacle and its color. The method was also reliable and steady, even when obstacles were moving.