LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping, 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 can extend the battery life of a robot and reduce the amount of raw data required to run localization algorithms. This enables more variations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the core of Lidar systems. It releases laser pulses into the environment. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, depending on the structure of the object. The sensor is able to measure the amount of time it takes to return each time and uses this information to determine distances. The sensor is typically placed on a rotating platform which allows it to scan the entire surrounding area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified according to their intended airborne or terrestrial application. Airborne lidar systems are usually connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is recorded using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are utilized by LiDAR systems to calculate the exact location of the sensor in the space and time. This information is used to create a 3D model of the environment.

LiDAR scanners are also able to identify various types of surfaces which is especially useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy, it will typically generate multiple returns. Typically, the first return is attributable to the top of the trees while the final return is attributed to the ground surface. If the sensor records these pulses in a separate way this is known as discrete-return LiDAR.

Distinte return scans can be used to analyze surface structure. For example, a forest region may result in one or two 1st and 2nd returns with the final large pulse representing bare ground. The ability to separate these returns and store them as a point cloud allows to create detailed terrain models.

Once an 3D model of the environment is constructed the best robot vacuum with lidar will be able to use this data to navigate. This involves localization and building a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the original map and updates the path plan according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment, and then determine its position in relation to that map. Engineers make use of this information for a range of tasks, such as planning routes and obstacle detection.

To allow SLAM to function the robot needs a sensor (e.g. a camera or laser), and a computer with the right software to process the data. You will also need an IMU to provide basic information about your position. The result is a system that can precisely track the position of your robot in a hazy environment.

The SLAM process is complex, and many different back-end solutions are available. No matter which one you choose, a successful SLAM system requires a constant interplay between the range measurement device, the software that extracts the data and the robot or vehicle itself. This is a highly dynamic process that is prone to an infinite amount of variability.

As the robot moves around, it adds new scans to its map. The SLAM algorithm compares these scans with the previous ones making use of a process known as scan matching. This allows loop closures to be created. The SLAM algorithm is updated with its estimated robot trajectory once the loop has been closed identified.

Another issue that can hinder SLAM is the fact that the surrounding changes over time. For instance, if a robot walks through an empty aisle at one point, and then encounters stacks of pallets at the next spot it will be unable to connecting these two points in its map. Handling dynamics are important in this situation and are a feature of many modern Lidar SLAM algorithms.

SLAM systems are extremely efficient at navigation and 3D scanning despite these challenges. It is particularly useful in environments where the robot isn't able to rely on GNSS for its positioning, such as an indoor factory floor. However, lidar Robot Navigation it is important to keep in mind that even a well-designed SLAM system can experience mistakes. It is essential to be able to spot these issues and comprehend how they impact the SLAM process to rectify them.

Mapping

The mapping function builds an outline of the robot's surrounding that includes the robot as well as its wheels and actuators, and everything else in its view. This map is used to perform localization, path planning, and obstacle detection. This is a field in which 3D Lidars are especially helpful, since they can be treated as a 3D Camera (with one scanning plane).

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

The greater the resolution of the sensor, then the more accurate will be the map. However there are exceptions to the requirement for maps with high resolution. For instance floor sweepers may not need the same level of detail as an industrial robot navigating large factory facilities.

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

GraphSLAM is another option, which utilizes a set of linear equations to represent constraints in the form of a diagram. The constraints are represented as an O matrix, and a X-vector. Each vertice of the O matrix contains an approximate distance from the X-vector's landmark. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements which means that all of the O and X vectors are updated to reflect new information about the robot.

Another useful mapping algorithm is SLAM+, which combines the use of odometry with mapping using an Extended Kalman Filter (EKF). The EKF alters the uncertainty of the robot's location as well as the uncertainty of the features that were mapped by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location, and also to update the map.

Obstacle Detection

A robot must be able see its surroundings to avoid obstacles and get to its destination. It employs sensors such as digital cameras, infrared scans, sonar and laser radar to determine the surrounding. It also makes use of an inertial sensor to measure its speed, position and its orientation. These sensors assist it in navigating in a safe manner and prevent collisions.

A key element of this process is obstacle detection that consists of the use of sensors to measure the distance between the robot and obstacles. The sensor can be mounted to the robot, a vehicle or a pole. It is important to keep in mind that the sensor could be affected by various factors, such as wind, rain, and fog. It is essential to calibrate the sensors prior each use.

The most important aspect of obstacle detection is to identify static obstacles. This can be done by using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low detection accuracy due to the occlusion caused by the spacing between different laser lines and the angle of the camera, which makes it difficult to detect static obstacles in a single frame. To overcome this issue multi-frame fusion was implemented to improve the accuracy of the static obstacle detection.

The method of combining roadside unit-based and obstacle detection using a vehicle camera has been proven to improve the efficiency of data processing and reserve redundancy for subsequent navigational operations, like path planning. This method provides an image of high-quality and reliable of the surrounding. In outdoor comparison experiments, the method was compared against other methods of obstacle detection such as YOLOv5 monocular ranging, VIDAR.

The results of the experiment showed that the algorithm could correctly identify the height and position of an obstacle, as well as its tilt and rotation. It was also able identify the color and size of an object. The algorithm was also durable and reliable, even when obstacles moved.