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LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will explain the concepts and show how they work by using an easy example where the robot reaches an objective within a row of plants.

LiDAR sensors are low-power devices that can prolong the life of batteries on robots and reduce the amount of raw data needed to run localization algorithms. This allows for more iterations of SLAM without overheating the GPU.

LiDAR Sensors

The heart of lidar systems is its sensor which emits pulsed laser light into the environment. The light waves hit objects around and bounce back to the sensor at a variety of angles, depending on the structure of the object. The sensor monitors the time it takes for each pulse to return, and utilizes that information to determine distances. The sensor is usually placed on a rotating platform which allows it to scan the entire area at high speed (up to 10000 samples per second).

lidar product sensors are classified by whether they are designed for airborne or terrestrial application. Airborne lidar systems are usually attached to helicopters, aircraft, or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually installed on a stationary robot platform.

To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is gathered using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use these sensors to compute the precise location of the sensor in space and time, which is later used to construct a 3D map of the environment.

LiDAR scanners can also identify different kinds of surfaces, which is particularly beneficial when mapping environments with dense vegetation. For instance, when the pulse travels through a canopy of trees, it will typically register several returns. The first return is usually associated with the tops of the trees, while the second one is attributed to the surface of the ground. If the sensor can record each pulse as distinct, it is called discrete return LiDAR.

Distinte return scans can be used to determine the structure of surfaces. For instance the forest may produce one or two 1st and 2nd returns with the final large pulse representing bare ground. The ability to separate and record these returns as a point-cloud permits detailed models of terrain.

Once a 3D map of the environment is created, the robot can begin to navigate based on this data. This process involves localization, creating an appropriate path to get to a destination and dynamic obstacle detection. The latter is the process of identifying new obstacles that aren't visible 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 surroundings and then determine its location in relation to that map. Engineers make use of this information for a range of tasks, including path planning and obstacle detection.

To utilize SLAM, your robot needs to have a sensor that gives range data (e.g. the laser or camera), and a computer with the appropriate software to process the data. You will also require an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can precisely track the position of your robot in an unknown environment.

The SLAM system is complex and offers a myriad of back-end options. Whatever solution you choose for the success of SLAM it requires a constant interaction between the range measurement device and the software that collects data, as well as the vehicle or robot. This is a highly dynamic process that can have an almost endless amount of variance.

As the robot vacuum lidar moves the area, it adds new scans to its map. The SLAM algorithm then compares these scans with previous ones using a process known as scan matching. This helps to establish loop closures. When a loop closure is discovered when loop closure is detected, the SLAM algorithm uses this information to update its estimate of the robot's trajectory.

The fact that the environment can change over time is another factor that complicates SLAM. For instance, if your robot is walking down an aisle that is empty at one point, but it comes across a stack of pallets at a different location it might have trouble finding the two points on its map. This is where handling dynamics becomes important and is a common characteristic of the modern Lidar SLAM algorithms.

SLAM systems are extremely efficient at navigation and 3D scanning despite these limitations. It is especially beneficial in situations where the robot isn't able to rely on GNSS for positioning for positioning, like an indoor factory floor. However, it's important to note that even a well-configured SLAM system may have errors. It is crucial to be able to spot these issues and comprehend how they affect the SLAM process in order to correct them.

Mapping

The mapping function creates a map of the robot's environment. This includes the robot as well as its wheels, actuators and best lidar vacuum everything else that falls within its vision field. The map is used for localization, path planning, and obstacle detection. This is a field in which 3D Lidars are especially helpful, since they can be treated as an 3D Camera (with one scanning plane).

The process of creating maps takes a bit of time, but the results pay off. The ability to build a complete, consistent map of the surrounding area allows it to conduct high-precision navigation, as being able to navigate around obstacles.

As a general rule of thumb, the higher resolution of the sensor, the more precise the map will be. However, not all robots need high-resolution maps: for example floor sweepers might not need the same degree of detail as an industrial robot navigating factories of immense size.

There are many different mapping algorithms that can be employed with LiDAR sensors. One popular algorithm is called Cartographer which utilizes a two-phase pose graph optimization technique to correct for drift and create an accurate global map. It is particularly effective when used in conjunction with odometry.

GraphSLAM is a different option, which utilizes a set of linear equations to represent the constraints in a diagram. The constraints are represented as an O matrix and a X vector, with each vertice of the O matrix containing the distance to a point on the X vector. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all O and X vectors are updated to account for the new observations made by the robot.

Another efficient mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF changes the uncertainty of the robot's position as well as the uncertainty of the features that were 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 must be able to perceive its surroundings so it can 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 manner and prevent collisions.

One important part of this process is obstacle detection that consists of 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 many factors, such as rain, wind, and fog. Therefore, it is important to calibrate the sensor before every use.

The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. This method isn't very accurate because of the occlusion created by the distance between laser lines and the camera's angular velocity. To overcome this problem, multi-frame fusion was used to improve the effectiveness of static obstacle detection.

The method of combining roadside unit-based as well as obstacle detection using a vehicle camera has been shown to improve the data processing efficiency and reserve redundancy for further navigational tasks, like path planning. The result of this method is a high-quality picture of the surrounding area that is more reliable than one frame. The method has been compared with other obstacle detection methods, such as YOLOv5 VIDAR, YOLOv5, robot vacuum lidar and monocular ranging in outdoor comparative tests.

The results of the study revealed that the algorithm was able accurately identify the height and location of an obstacle, as well as its rotation and tilt. It was also able detect the size and color of an object. The method also exhibited solid stability and reliability even in the presence of moving obstacles.