LiDAR Robot Navigation

LiDAR robot navigation is a sophisticated combination of localization, mapping, and path planning. This article will explain the concepts and explain how they work using an example in which the robot achieves an objective within a row of plants.

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

LiDAR Sensors

The core of a lidar system is its sensor that emits laser light in the environment. These light pulses strike objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor determines how long it takes each pulse to return, and utilizes that information to calculate distances. The sensor is typically mounted on a rotating platform, permitting it to scan the entire area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified based on whether they're intended for use in the air or on the ground. Airborne lidar systems are typically connected to aircrafts, helicopters, or UAVs. (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 using an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to calculate the exact location of the sensor within space and time. The information gathered is used to build a 3D model of the surrounding.

LiDAR scanners can also identify different types of surfaces, which is especially useful when mapping environments that have dense vegetation. For instance, if the pulse travels through a canopy of trees, it is common for it to register multiple returns. Typically, the first return is attributed to the top of the trees, and the last one is attributed to the ground surface. If the sensor records each pulse as distinct, this is known as discrete return LiDAR.

Discrete return scanning can also be useful in analysing the structure of surfaces. For instance, a forest area could yield an array of 1st, 2nd, and 3rd returns, with a final, large pulse representing the bare ground. The ability to separate and record these returns in a point-cloud allows for detailed terrain models.

Once a 3D model of the environment is created, the robot will be able to use this data to navigate. This involves localization as well as making a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the map that was created and updates the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its environment, and then identifimpact the SLAM process in order to fix them.

Mapping

The mapping function creates a map of a robot's environment. This includes the Dreame F9 Robot Vacuum Cleaner with Mop: Powerful 2500Pa (www.robotvacuummops.com) as well as its wheels, actuators and everything else that falls within its vision field. This map is used to aid in the localization of the robot, route planning and obstacle detection. This is an area in which 3D lidars are particularly helpful, as they can be used as the equivalent of a 3D camera (with a single scan plane).

Map creation is a time-consuming process but it pays off in the end. The ability to create 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 of the sensor, the more accurate the map will be. Not all robots require high-resolution maps. For instance floor sweepers might not require the same level detail as an industrial robotics system that is navigating factories of a large size.

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 accurate global map. It is especially efficient when combined with Odometry data.

GraphSLAM is another option, that uses a set linear equations to model the constraints in diagrams. The constraints are represented by an O matrix, and an vector X. Each vertice in the O matrix contains the distance to a landmark on X-vector. A GraphSLAM update consists of an array of additions and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to reflect new robot observations.

SLAM+ is another useful mapping algorithm that combines odometry with 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 recorded by the sensor. The mapping function is able to utilize this information to improve its own position, which allows it to update the underlying map.

Obstacle Detection

A robot needs to be able to sense its surroundings to avoid obstacles and reach its goal point. It employs sensors such as digital cameras, infrared scans, sonar and laser radar to detect the environment. Additionally, it employs inertial sensors to measure its speed and position as well as its orientation. These sensors aid in navigation in a safe way and prevent collisions.

A range sensor is used to measure the distance between the robot vacuum with lidar and camera and the obstacle. The sensor can be positioned on the robot, in a vehicle or on poles. It is important to remember that the sensor may be affected by a variety of factors, such as rain, wind, or fog. It is important to calibrate the sensors prior each use.

A crucial step in obstacle detection is identifying static obstacles, which can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very accurate because of the occlusion caused by the distance between the laser lines and the camera's angular speed. To address this issue, multi-frame fusion was used to improve the accuracy of static obstacle detection.

The technique of combining roadside camera-based obstruction detection with the vehicle camera has proven to increase the efficiency of data processing. It also allows redundancy for other navigation operations such as planning a path. The result of this technique is a high-quality picture of the surrounding area that is more reliable than a single frame. In outdoor tests, the method was compared to other methods for detecting obstacles such as YOLOv5, monocular ranging and VIDAR.

The results of the experiment showed that the algorithm was able to correctly identify the position and height of an obstacle, in addition to its rotation and tilt. It also had a good performance in identifying the size of obstacles and its color. The method also showed excellent stability and durability even when faced with moving obstacles.