LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of mapping, localization and path planning. This article will present these concepts and show how they interact using an example of a robot reaching a goal in a row of crop.

LiDAR sensors are low-power devices that extend the battery life of robots and reduce the amount of raw data required to run localization algorithms. This allows for a greater number of iterations of SLAM without overheating GPU.

LiDAR Sensors

The core of lidar systems is their sensor that emits laser light in the environment. The light waves bounce off objects around them at different angles based on their composition. The sensor is able to measure the amount of time it takes for each return, which is then used to calculate distances. The sensor is usually placed on a rotating platform, allowing it to quickly scan the entire area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified by their intended applications in the air or on land. Airborne lidars are usually mounted on helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR systems are generally mounted on a static robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is captured by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems to calculate the precise position of the sensor within space and time. This information is used to create a 3D representation of the surrounding environment.

lidar vacuum mop scanners are also able to identify different kinds of surfaces, which is especially useful when mapping environments with dense vegetation. When a pulse crosses a forest canopy it will usually generate multiple returns. The first return is usually attributed to the tops of the trees, while the second is associated with the surface of the ground. If the sensor records these pulses separately this is known as discrete-return lidar navigation.

Discrete return scanning can also be helpful in studying the structure of surfaces. For example the forest may produce an array of 1st and 2nd returns, with the final big pulse representing the ground. The ability to separate these returns and record them as a point cloud allows for the creation of detailed terrain models.

Once a 3D map of the surrounding area has been created, the robot can begin to navigate based on this data. This process involves localization, building an appropriate path to get to a destination and dynamic obstacle detection. This process 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 mapping and localization) is an algorithm that allows your robot to map its surroundings and then determine its location relative to that map. Engineers utilize this information to perform a variety of tasks, including path planning and obstacle detection.

To be able to use SLAM, your robot needs to be equipped with a sensor that can provide range data (e.g. a camera or laser) and a computer running the right software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that can precisely track the position of your robot in a hazy environment.

The SLAM system is complex and there are a variety of back-end options. Whatever solution you select for a successful SLAM, it requires a constant interaction between the range measurement device and the software that collects data and also the vehicle or robot. This is a highly dynamic process that has an almost 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 the previous ones using a method known as scan matching. This allows loop closures to be established. The SLAM algorithm is updated with its estimated robot trajectory once loop closures are detected.

The fact that the environment can change over time is a further factor that makes it more difficult for LiDAR Robot Navigation SLAM. For instance, if a robot walks down an empty aisle at one point, and is then confronted by pallets at the next location it will have a difficult time matching these two points in its map. This is where handling dynamics becomes crucial and is a typical feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient in navigation and 3D scanning despite these challenges. It is especially beneficial in environments that don't permit the robot to rely on GNSS positioning, like an indoor factory floor. It is important to keep in mind that even a properly configured SLAM system can experience mistakes. To fix these issues, it is important to be able detect them and comprehend their impact on the SLAM process.

Mapping

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

The process of building maps takes a bit of time however the results pay off. The ability to create a complete and coherent map of the environment around a robot allows it to navigate with high precision, as well as around obstacles.

As a rule of thumb, the higher resolution the sensor, more accurate the map will be. Not all robots require maps with high resolution. For instance floor sweepers might not require the same level detail as a robotic system for industrial use operating in large factories.

There are a variety of mapping algorithms that can be utilized with LiDAR sensors. One popular algorithm is called Cartographer which utilizes two-phase pose graph optimization technique to correct for drift and create a consistent global map. It is particularly effective when paired with odometry.

Another option is GraphSLAM, which uses linear equations to model constraints in graph. The constraints are represented as an O matrix and an one-dimensional X vector, each vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The result is that both the O and X Vectors are updated in order to reflect the latest observations made by the robot.

SLAM+ is another useful mapping algorithm that combines odometry with mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current position but also the uncertainty of the features that were recorded by the sensor. This information can be utilized by the mapping function to improve its own estimation of its position and update the map.

Obstacle Detection

A robot must be able detect its surroundings so that it can avoid obstacles and get to its destination. It makes use of sensors such as digital cameras, infrared scanners, sonar and laser radar to sense its surroundings. It also uses inertial sensor to measure its speed, location and orientation. These sensors assist it in navigating in a safe way and avoid collisions.

A range sensor is used to determine the distance between an obstacle and a robot. The sensor can be placed on the robot, in a vehicle or on poles. It is important to remember that the sensor is affected by a myriad of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor prior each use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. This method is not very accurate because of the occlusion caused by the distance between laser lines and the camera's angular speed. To overcome this problem, multi-frame fusion was used to increase the effectiveness of static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been proven to improve the efficiency of data processing and reserve redundancy for subsequent navigational tasks, like path planning. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. The method has been compared with other obstacle detection methods including YOLOv5, VIDAR, and monocular ranging in outdoor comparison experiments.

The results of the experiment showed that the algorithm could accurately determine the height and position of an obstacle, as well as its tilt and rotation. It also had a great ability to determine the size of an obstacle and its color. The method was also robust and steady, even when obstacles moved.