What Lidar Robot Navigation Will Be Your Next Big Obsession

LiDAR Robot Navigation

LiDAR robots navigate by using the combination of localization and mapping, and also path planning. This article will present these concepts and explain how they interact using an easy example of the robot achieving its goal in the middle of a row of crops.

LiDAR sensors are low-power devices that can extend the battery life of robots and decrease the amount of raw data required for localization algorithms. This allows for more variations of the SLAM algorithm without overheating the GPU.

lidar robot vacuum Sensors

The heart of a lidar vacuum mop system is its sensor which emits pulsed laser light into the surrounding. These pulses bounce off objects around them in different angles, based on their composition. The sensor measures how long it takes for each pulse to return and uses that data to calculate distances. Sensors are mounted on rotating platforms that allow them to scan the area around them quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified based on whether they're intended for applications in the air or on land. Airborne lidars are usually mounted on helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are typically mounted 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 typically captured through a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of these sensors to compute the exact location of the sensor in space and time, which is then used to build up a 3D map of the surrounding area.

LiDAR scanners are also able to identify different surface types which is especially useful for mapping environments with dense vegetation. For instance, when an incoming pulse is reflected through a canopy of trees, it will typically register several returns. The first return is attributable to the top of the trees, and the last one is attributed to the ground surface. If the sensor captures each peak of these pulses as distinct, it is referred to as discrete return LiDAR.

Distinte return scanning can be helpful in analysing the structure of surfaces. For instance, a forest region may yield one or two 1st and 2nd return pulses, with the final large pulse representing bare ground. The ability to separate and store these returns as a point cloud permits detailed terrain models.

Once a 3D model of environment is constructed and the robot is able to use this data to navigate. This involves localization as well as creating a path to reach a navigation "goal." It also involves dynamic obstacle detection. This is the process that detects new obstacles that are not listed in the map's original version and adjusts the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and LiDAR Robot Navigation mapping) is an algorithm that allows your robot to build a map of its environment and then determine the location of its position relative to the map. Engineers use this information for a variety of tasks, including path planning and obstacle detection.

To utilize SLAM, your robot needs to be equipped with a sensor that can provide 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 will also need an IMU to provide basic information about your position. The system will be able to track your robot's location accurately in an undefined environment.

The SLAM process is extremely complex, and many different back-end solutions are available. Whatever solution you select, a successful SLAM system requires a constant interplay between the range measurement device and the software that collects the data, and the vehicle or robot itself. This is a dynamic process with a virtually unlimited variability.

As the robot moves the area, it adds new scans to its map. The SLAM algorithm compares these scans to prior ones using a process called scan matching. This assists in establishing loop closures. The SLAM algorithm is updated with its estimated robot trajectory once a loop closure has been discovered.

Another factor that makes SLAM is the fact that the environment changes over time. For instance, if a robot is walking down an empty aisle at one point, and is then confronted by pallets at the next location it will be unable to connecting these two points in its map. This is where handling dynamics becomes critical and is a common characteristic of modern Lidar SLAM algorithms.

SLAM systems are extremely effective in 3D scanning and navigation despite the challenges. It is particularly beneficial in environments that don't let the robot rely on GNSS position, such as an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system may experience mistakes. It is essential to be able to detect these errors and understand how they impact the SLAM process to rectify them.

Mapping

The mapping function creates a map for a robot's environment. This includes the robot, its wheels, actuators and everything else that is within its vision field. This map is used for the localization, planning of paths and obstacle detection. This is an area where 3D lidars can be extremely useful, as they can be utilized like a 3D camera (with a single scan plane).

Map creation is a long-winded process but it pays off in the end. The ability to create a complete, consistent map of the robot's environment 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 precise the map will be. However, not all robots need high-resolution maps. For example, a floor sweeper may not require the same degree of detail as an industrial robot that is navigating large factory facilities.

There are a variety of mapping algorithms that can be used with LiDAR sensors. Cartographer is a popular algorithm that uses a two-phase pose graph optimization technique. It corrects for drift while maintaining an unchanging global map. It is particularly beneficial when used in conjunction with Odometry data.

GraphSLAM is a different option, which uses a set of linear equations to model the constraints in a diagram. The constraints are modelled 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 consists of a series of additions and subtraction operations on these matrix elements which means that all of the O and X vectors are updated to accommodate new observations of the robot.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's position as well as the uncertainty of the features that were recorded by the sensor. The mapping function is able to utilize this information to estimate its own location, allowing it to update the base map.

Obstacle Detection

A robot should be able to see its surroundings so that it can overcome obstacles and reach its destination. It uses sensors such as digital cameras, infrared scans sonar, laser radar and others to determine the surrounding. It also uses inertial sensors to monitor its position, speed and the direction. These sensors help it navigate without danger and avoid collisions.

A range sensor is used to gauge the distance between the robot and the obstacle. The sensor can be positioned on the robot, in a vehicle or on poles. It is crucial to keep in mind that the sensor can be affected by a myriad of factors like rain, wind and fog. Therefore, it is essential to calibrate the sensor prior to every use.

The most important aspect of obstacle detection is identifying static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method isn't very precise due to the occlusion created by the distance between the laser lines and the camera's angular speed. To overcome this issue multi-frame fusion was employed to increase the accuracy of static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been shown to improve the data processing efficiency and reserve redundancy for future navigational tasks, 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. The method has been compared with other obstacle detection techniques like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging in outdoor comparative tests.

The results of the test revealed that the algorithm was able to accurately identify the position and height of an obstacle, as well as its rotation and tilt. It was also able to detect the color and size of an object. The method was also robust and reliable even when obstacles were moving.