LiDAR Robot Navigation

LiDAR robots move using a combination of localization and mapping, as well as path planning. This article will explain the concepts and show how they function using a simple example where the robot achieves the desired goal within a row of plants.

LiDAR sensors have low power demands allowing them to increase the battery life of a robot and reduce the amount of raw data required for localization algorithms. This allows for more versions of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The core of lidar systems is its sensor, which emits laser light in the surrounding. The light waves bounce off surrounding objects at different angles depending on their composition. The sensor determines how long it takes each pulse to return and then uses that data to determine distances. The sensor is typically placed on a rotating platform which allows it to scan the entire area at high speed (up to 10000 samples per second).

LiDAR sensors are classified by the type of sensor they are designed for applications in the air or on land. Airborne lidar systems are commonly 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 captured by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use these sensors to compute the exact location of the sensor in space and time, which is then used to create an image of 3D of the surrounding area.

lidar robot vacuum scanners are also able to detect different types of surface and types of surfaces, which is particularly useful for mapping environments with dense vegetation. When a pulse passes a forest canopy it will usually produce multiple returns. Typically, the first return is attributed to the top of the trees while the last return is attributed to the ground surface. If the sensor captures each peak of these pulses as distinct, this is called discrete return LiDAR.

Distinte return scanning can be useful in analysing the structure of surfaces. For instance, a forested area could yield the sequence of 1st 2nd and 3rd return, with a last large pulse representing the bare ground. The ability to divide these returns and save them as a point cloud allows for the creation of precise terrain models.

Once an 3D map of the surrounding area has been created, the robot can begin to navigate using this data. This involves localization and building a path that will reach a navigation "goal." It also involves dynamic obstacle detection. This is the process that identifies new obstacles not included in the original map and adjusts the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an image of its surroundings and then determine where it is relative to the map. Engineers use this information for a range of tasks, including the planning of routes and obstacle detection.

To be able to use SLAM your robot vacuum with lidar has to have a sensor that provides range data (e.g. A computer that has the right software for processing the data and a camera or a laser are required. You also need an inertial measurement unit (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 system is complex and there are many different back-end options. Whatever solution you choose to implement the success of SLAM it requires constant interaction between the range measurement device and the software that extracts the data and also the robot or vehicle. This is a dynamic procedure that is almost indestructible.

As the robot moves the area, it adds new scans to its map. The SLAM algorithm compares these scans with previous ones by using a process called scan matching. This helps to establish loop closures. The SLAM algorithm is updated with its estimated robot trajectory when a loop closure has been discovered.

The fact that the environment changes over time is a further factor that makes it more difficult for SLAM. If, for example, your robot is navigating an aisle that is empty at one point, but it comes across a stack of pallets at a different location it may have trouble matching the two points on its map. This is when handling dynamics becomes important and is a typical feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient at navigation and 3D scanning despite these limitations. It is particularly useful in environments that do not let the robot depend on GNSS for positioning, such as an indoor factory floor. However, it is important to keep in mind that even a well-designed SLAM system can experience mistakes. To correct these mistakes it is crucial to be able to spot them and understand their impact on the SLAM process.

Mapping

The mapping function creates a map of the robot's surrounding, which includes the robot itself as well as its wheels and actuators and everything else that is in the area of view. The map is used for the localization, planning of paths and obstacle detection. This is an area in which 3D lidars can be extremely useful because they can be used like an actual 3D camera (with one scan plane).

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

As a rule of thumb, the higher resolution the sensor, more precise the map will be. Not all robots require maps with high resolution. For example floor sweepers might not require the same level detail as an industrial robotics system navigating large factories.

This is why there are many different mapping algorithms to use with LiDAR sensors. One of the most popular algorithms is Cartographer which employs the two-phase pose graph optimization technique to adjust for drift and keep an accurate global map. It is particularly effective when used in conjunction with odometry.

GraphSLAM is another option, which uses a set of linear equations to model the constraints in diagrams. The constraints are represented as an O matrix and a the X vector, with every vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM Update is a series of additions and subtractions on these matrix elements. The result is that all O and X Vectors are updated to reflect the latest observations made by the robot.

Another helpful mapping algorithm is SLAM+, which combines the use of odometry with mapping using an Extended Kalman Filter (EKF). The EKF changes the uncertainty of the robot's location as well as the uncertainty of the features mapped by the sensor. The mapping function is able to make use of this information to improve its own position, which allows it to update the base map.

Obstacle Detection

A robot needs to be able to perceive its surroundings in order to avoid obstacles and reach its goal point. It makes use of sensors such as digital cameras, infrared scanners, laser radar and sonar to detect its environment. It also uses inertial sensor to measure its position, speed and its orientation. These sensors assist it in navigating in a safe and secure manner and prevent collisions.

One important part of this process is the detection of obstacles, which involves the use of sensors to measure the distance between the robot and obstacles. The sensor can be positioned on the robot, in the vehicle, LiDAR Robot Navigation or on a pole. It is crucial to remember that the sensor is affected by a variety of elements like rain, wind and fog. Therefore, it is crucial to calibrate the sensor prior each use.

The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method isn't particularly accurate because of the occlusion caused by the distance between the laser lines and the camera's angular velocity. To overcome this problem, a method called multi-frame fusion has been used to increase the accuracy of detection of static obstacles.

The method of combining roadside unit-based and obstacle detection using a vehicle camera has been proven to increase the efficiency of processing data and reserve redundancy for subsequent navigational tasks, like path planning. This method creates a high-quality, reliable image of the environment. The method has been tested against other obstacle detection methods like YOLOv5, VIDAR, and monocular ranging in outdoor tests of comparison.

The results of the test showed that the algorithm could accurately determine the height and location of an obstacle, as well as its tilt and rotation. It also showed a high performance in identifying the size of an obstacle and its color. The method also exhibited excellent stability and durability even in the presence of moving obstacles.