LiDAR Robot Navigation

LiDAR robots navigate by using a combination of localization and mapping, and also path planning. This article will explain these concepts and demonstrate how they work together using a simple example of the robot achieving a goal within a row of crop.

LiDAR sensors are relatively low power demands allowing them to extend a robot's battery life and reduce the need for raw data for localization algorithms. This allows for more repetitions of SLAM without overheating GPU.

LiDAR 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, based on the composition of the object. The sensor measures the amount of time required to return each time, which is then used to calculate distances. The sensor is typically placed on a rotating platform allowing it to quickly scan the entire area at high speed (up to 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 connected to helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually installed on a robot platform that is stationary.

To accurately measure distances, the sensor must know the exact position of the robot vacuum cleaner with lidar at all times. This information is recorded by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by LiDAR systems to determine the exact position of the sensor within the space and time. The information gathered is used to build a 3D model of the surrounding environment.

LiDAR scanners can also be used to recognize different types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. When a pulse crosses a forest canopy it will usually generate multiple returns. The first one is typically attributable to the tops of the trees while the last is attributed with the ground's surface. If the sensor records each pulse as distinct, this is referred to as discrete return LiDAR.

The Discrete Return scans can be used to analyze the structure of surfaces. For instance, a forested area could yield an array of 1st, 2nd and 3rd returns with a last large pulse representing the ground. The ability to divide these returns and save them as a point cloud allows for the creation of precise terrain models.

Once a 3D map of the surroundings has been built, the robot can begin to navigate using this data. This process involves localization, creating a path to reach a navigation 'goal,' and dynamic obstacle detection. This is the process that detects new obstacles that were not present 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 create an image of its surroundings and then determine the location of its position relative to the map. Engineers use this information for a variety of tasks, such as the planning of routes and obstacle detection.

To enable SLAM to function the robot needs an instrument (e.g. a camera or laser) and a computer running the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that will accurately determine the location of your robot in an unspecified environment.

The SLAM system is complex and there are many different back-end options. Regardless of which solution you select the most effective SLAM system requires a constant interplay between the range measurement device, the software that extracts the data, and the robot Vacuum Cleaner lidar or vehicle itself. It is a dynamic process with a virtually unlimited variability.

As the robot moves, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones using a process known as scan matching. This assists in establishing loop closures. When a loop closure is identified, the SLAM algorithm makes use of this information to update its estimated robot trajectory.

The fact that the environment changes over time is a further factor that complicates SLAM. For instance, if your robot is walking through an empty aisle at one point and is then confronted by pallets at the next point it will have a difficult time matching these two points in its map. This is where handling dynamics becomes crucial and is a typical characteristic of the modern Lidar SLAM algorithms.

Despite these issues however, a properly designed SLAM system is incredibly effective for navigation and 3D scanning. It is particularly beneficial in situations where the robot isn't able to rely 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 may have errors. To correct these errors it is essential to be able to spot them and comprehend their impact on the SLAM process.

Mapping

The mapping function creates an outline of the robot's environment which includes the robot including its wheels and actuators and everything else that is in the area of view. The map is used for the localization of the robot, route planning and obstacle detection. This is a field where 3D Lidars are especially helpful because they can be regarded as a 3D Camera (with one scanning plane).

Map building is a long-winded process but it pays off in the end. The ability to build a complete and coherent map of a robot's environment allows it to navigate with high precision, as well as over obstacles.

The greater the resolution of the sensor, the more precise will be the map. However it is not necessary for all robots to have maps with high resolution. For instance, a floor sweeper may not require the same level 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 of the most popular algorithms is Cartographer which utilizes two-phase pose graph optimization technique to correct for drift and create a uniform global map. It is particularly useful when combined with Odometry.

Another option is GraphSLAM, which uses linear equations to model the constraints of graph. The constraints are represented by an O matrix, and an the X-vector. Each vertice of the O matrix is the distance to a landmark on X-vector. A GraphSLAM update consists of an array of additions and robot vacuum cleaner lidar subtraction operations on these matrix elements which means that all of the O and X vectors are updated to reflect new robot observations.

SLAM+ is another useful mapping algorithm that combines odometry and 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 were mapped by the sensor. This information can be used by the mapping function to improve its own estimation of its location and to update the map.

Obstacle Detection

A robot vacuum lidar must be able see its surroundings to avoid obstacles and reach its goal. It makes use of sensors such as digital cameras, infrared scanners laser radar and sonar to sense its surroundings. It also utilizes an inertial sensor to measure its speed, location and the direction. These sensors assist it in navigating in a safe way and prevent collisions.

A range sensor is used to measure the distance between an obstacle and a robot. The sensor can be mounted on the robot, in a vehicle or on poles. It is crucial to remember that the sensor is affected by a myriad of factors, including wind, rain and fog. It is important to calibrate the sensors prior to every 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 induced by the distance between the laser lines and the camera's angular speed. To address this issue, a technique of multi-frame fusion has been used to increase the accuracy of detection of static obstacles.

The technique of combining roadside camera-based obstacle detection with the vehicle camera has proven to increase the efficiency of processing data. It also allows redundancy for other navigation operations, like the planning of a path. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. In outdoor robot vacuum cleaner lidar comparison experiments the method was compared to other methods of obstacle detection such as YOLOv5 monocular ranging, VIDAR.

The results of the experiment showed that the algorithm was able accurately identify the location and height of an obstacle, in addition to its rotation and tilt. It also had a great performance in detecting the size of an obstacle and its color. The method was also robust and stable, even when obstacles were moving.