Lidar Robot Navigation 101"The Complete" Guide For Beginners

LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of localization, mapping, and path planning. This article will explain the concepts and demonstrate how they function using an example in which the robot vacuum with lidar and camera reaches a goal within a row of plants.

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

LiDAR Sensors

The central component of lidar systems is their sensor, which emits pulsed laser light into the surrounding. The light waves bounce off surrounding objects in different angles, based on their composition. The sensor monitors the time it takes each pulse to return and then uses that data to determine distances. Sensors are mounted on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified based on whether they're intended for use in the air or on the ground. Airborne lidar systems are typically mounted on aircrafts, helicopters, or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are typically mounted on a static robot platform.

To accurately measure distances the sensor must always know the exact location of the robot. This information is usually gathered by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems utilize these sensors to compute the exact location of the sensor in space and time. This information is later used to construct an 3D map of the environment.

LiDAR scanners can also detect different kinds of surfaces, which is particularly beneficial when mapping environments with dense vegetation. For instance, when an incoming pulse is reflected through a forest canopy it is common for it to register multiple returns. Usually, the first return is attributed to the top of the trees, while the final return is related to the ground surface. If the sensor captures these pulses separately, it is called discrete-return LiDAR.

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

Once an 3D map of the environment has been built, the robot can begin to navigate using this information. This involves localization and LiDAR Robot Navigation making a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This is the process of identifying obstacles that aren't visible on the original map and then updating the plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment, and then identify its location relative to that map. Engineers utilize the information to perform a variety of tasks, such as the planning of routes and obstacle detection.

For SLAM to work it requires an instrument (e.g. laser or camera) and a computer with the right software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can accurately track the location of your robot in an unknown environment.

The SLAM process is extremely complex, and many different back-end solutions are available. Whatever option you select for the success of SLAM, it requires constant interaction between the range measurement device and the software that extracts data, as well as the robot or vehicle. This is a highly dynamic procedure that is prone to an endless amount of variance.

As the robot moves about and around, it adds new scans to its map. The SLAM algorithm then compares these scans with earlier ones using a process known as scan matching. This allows loop closures to be identified. If a loop closure is discovered when loop closure is detected, the SLAM algorithm makes use of this information to update its estimated robot trajectory.

Another factor that complicates SLAM is the fact that the environment changes in time. If, for instance, your robot is walking down an aisle that is empty at one point, and then comes across a pile of pallets at a different location it might have trouble connecting the two points on its map. This is where handling dynamics becomes crucial, and this is a typical feature of the modern Lidar SLAM algorithms.

Despite these issues, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in environments that don't allow the robot to rely on GNSS-based position, such as an indoor factory floor. However, it is important to remember that even a well-configured SLAM system can experience mistakes. To correct these mistakes it is crucial to be able to spot the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function builds an outline of the robot's surroundings that includes the robot as well as its wheels and actuators, and everything else in the area of view. The map is used to perform localization, path planning and obstacle detection. This is an area where 3D lidars are extremely helpful because they can be utilized like a 3D camera (with one scan plane).

Map creation is a time-consuming process, but it pays off in the end. The ability to build a complete, coherent 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 greater resolution the sensor, the more accurate the map will be. Not all robots require high-resolution maps. For example a floor-sweeping robot might not require the same level detail as an industrial robotic system navigating large factories.

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

GraphSLAM is another option, which utilizes a set of linear equations to represent constraints in diagrams. The constraints are represented as an O matrix, as well as an the X-vector. Each vertice of the O matrix contains an approximate distance from the X-vector's landmark. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, with the end result being that all of the X and O 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 changes the uncertainty of the robot's position as well as the uncertainty of the features that were drawn by the sensor. The mapping function can then utilize this information to better estimate its own location, allowing it to update the underlying map.

Obstacle Detection

A robot needs to be able to see its surroundings so that it can avoid obstacles and reach its destination. It employs sensors such as digital cameras, infrared scans, sonar and laser radar to detect the environment. It also utilizes an inertial sensors to monitor its speed, position and its orientation. These sensors help it navigate in a safe manner and avoid collisions.

A range sensor is used to gauge the distance between the robot and the obstacle. The sensor can be mounted on the robot, inside an automobile or on poles. It is important to keep in mind that the sensor could be affected by a myriad of factors like rain, wind and fog. Therefore, it is crucial to calibrate the sensor prior each use.

A crucial step in obstacle detection is the identification of static obstacles, which can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very precise due to the occlusion induced by the distance between laser lines and the camera's angular velocity. To solve this issue, a technique of multi-frame fusion was developed to improve the detection accuracy of static obstacles.

The method of combining roadside unit-based as well as obstacle detection using a vehicle camera has been proven to improve the data processing efficiency and reserve redundancy for subsequent navigation operations, such as 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 like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging in outdoor comparative tests.

The results of the test proved that the algorithm was able accurately identify the position and height of an obstacle, as well as its tilt and rotation. It was also able determine the color and size of an object. The method was also robust and stable even when obstacles moved.