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LiDAR Robot Navigation

LiDAR robots navigate using a combination of localization and mapping, as well as path planning. This article will explain these concepts and show how they interact using an example of a robot achieving its goal in a row of crop.

LiDAR sensors are low-power devices that can prolong 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 robot vacuum Sensors

The sensor is the core of the Lidar system. It releases laser pulses into the environment. The light waves hit objects around and bounce back to the sensor at various angles, depending on the composition of the object. The sensor determines how long it takes for each pulse to return, and uses that data to determine distances. Sensors are placed on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified by their intended airborne or terrestrial application. Airborne lidar systems are typically connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is typically installed on a robot platform that is stationary.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually gathered through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. LiDAR systems utilize these sensors to compute the exact location of the sensor in space and time, which is then used to build up an image of 3D of the surrounding area.

LiDAR scanners can also detect different kinds of surfaces, which is especially useful when mapping environments that have dense vegetation. When a pulse passes a forest canopy, it is likely to produce multiple returns. The first return is attributed to the top of the trees, and the last one is associated with the ground surface. If the sensor records these pulses separately, it is called discrete-return LiDAR.

Discrete return scanning can also be helpful in studying the structure of surfaces. For instance forests can yield one or two 1st and 2nd returns, with the last one representing bare ground. The ability to separate and store these returns as a point cloud allows for precise models of terrain.

Once an 3D map of the environment has been built and the robot is able to navigate using this information. This process involves localization, constructing a path to reach a navigation 'goal and dynamic obstacle detection. The latter is the process of identifying obstacles that aren't visible in the map originally, and then updating the plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings, and then identify its location in relation to the map. Engineers utilize the information to perform a variety of tasks, such as planning a path and identifying obstacles.

To use SLAM the robot needs to have a sensor that provides range data (e.g. laser or camera) and a computer with the right software to process the data. 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 a hazy environment.

The SLAM process is extremely complex and a variety of back-end solutions exist. Whatever option you choose for a successful SLAM is that it requires constant interaction between the range measurement device and the software that collects data and also the robot or vehicle. This is a dynamic procedure with almost infinite variability.

As the robot moves around the area, it adds new scans to its map. The SLAM algorithm analyzes these scans against prior ones making use of a process known as scan matching. This allows loop closures to be identified. When a loop closure has been discovered it is then the SLAM algorithm makes use of this information to update its estimated robot trajectory.

Another factor that complicates SLAM is the fact that the surrounding changes in 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 be unable to matching these two points in its map. This is where handling dynamics becomes important, and this is a common feature of the modern Lidar SLAM algorithms.

SLAM systems are extremely efficient in 3D scanning and navigation despite these challenges. It is particularly useful in environments where the robot vacuum cleaner with lidar can't depend on GNSS to determine its position for example, an indoor factory floor. It is important to keep in mind that even a properly configured SLAM system can be prone to errors. To fix these issues, it is important to be able to spot them and comprehend their impact on the SLAM process.

Mapping

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

The map building process can take some time however the results pay off. The ability to create a complete, consistent map of the robot's environment allows it to carry out high-precision navigation as well as navigate around obstacles.

As a general rule of thumb, the higher resolution the sensor, the more accurate the map will be. However there are exceptions to the requirement for maps with high resolution. For instance floor sweepers may not require the same amount of detail as an industrial robot that is navigating factories with huge facilities.

There are a variety of mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which utilizes a two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is particularly effective when combined with the odometry.

GraphSLAM is a second option that uses a set linear equations to represent constraints in the form of a diagram. The constraints are represented as an O matrix, and a the X-vector. Each vertice of the O matrix represents the distance to a landmark on X-vector. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The result is that all O and X Vectors are updated to account for the new observations made by the robot vacuum with lidar (check out the post right here).

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 of the features that have been mapped by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location and to update the map.

Obstacle Detection

A robot must be able see its surroundings to avoid obstacles and reach its destination. It employs sensors such as digital cameras, infrared scans laser radar, and sonar to sense the surroundings. Additionally, it employs inertial sensors to measure its speed, position and orientation. These sensors allow it to navigate in a safe manner and avoid collisions.

One important part of this process is the detection of obstacles that involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be mounted to the robot, a vehicle or even a pole. It is important to remember that the sensor can be affected by a variety of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor prior to each use.

The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method isn't very precise due to the occlusion caused by the distance between laser lines and the camera's angular speed. To address this issue multi-frame fusion was implemented to increase the accuracy of the static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been proven to increase the data processing efficiency and reserve redundancy for further navigational tasks, like path planning. The result of this technique is a high-quality picture of the surrounding environment that is more reliable than a single frame. The method has been tested against other obstacle detection methods, such as YOLOv5 VIDAR, YOLOv5, as well as monocular ranging, in outdoor tests of comparison.

The results of the experiment showed that the algorithm could correctly identify the height and position of obstacles as well as its tilt and rotation. It was also able determine the color Robot vacuum with lidar and size of an object. The method was also robust and steady, even when obstacles were moving.