LiDAR Robot Navigation

LiDAR robot navigation is a sophisticated combination of localization, mapping and path planning. This article will introduce the concepts and explain how they work using an easy example where the robot is able to reach a goal within the space of a row of plants.

LiDAR sensors are relatively low power requirements, which allows them to increase the battery life of a robot and reduce the amount of raw data required for localization algorithms. This allows for more repetitions of SLAM without overheating GPU.

LiDAR Sensors

The core of lidar systems is their sensor which emits laser light pulses into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, depending on the composition of the object. The sensor measures how long it takes each pulse to return, and uses that data to calculate distances. The sensor is typically mounted on a rotating platform, permitting it to scan the entire area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on the type of sensor they're designed for, whether use in the air or Lidar Robot on the ground. Airborne lidars are often mounted on helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is typically captured using a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems in order to determine the exact location of the sensor in the space and time. This information is used to create a 3D representation of the surrounding environment.

LiDAR scanners are also able to identify different surface types which is especially beneficial for mapping environments with dense vegetation. When a pulse passes through a forest canopy, it will typically register 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 these pulses in a separate way, it is called discrete-return LiDAR.

Discrete return scans can be used to study surface structure. For instance forests can produce an array of 1st and 2nd returns with the last one representing the ground. The ability to separate and record these returns as a point cloud allows for detailed terrain models.

Once a 3D map of the environment has been built, the robot can begin to navigate using this data. This process involves localization, constructing a path to get to a destination and dynamic obstacle detection. This is the method of identifying new obstacles that aren't visible in the map originally, and then updating the plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings and then identify its location relative to that map. Engineers use this information for a variety of tasks, including path planning and LiDAR Robot obstacle detection.

To be able to use SLAM, your robot needs to have a sensor that provides range data (e.g. A computer that has the right software for processing the data and cameras or lasers are required. 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 an unknown environment.

The SLAM system is complex and offers a myriad of back-end options. No matter which solution you choose to implement a successful SLAM it requires constant interaction between the range measurement device and the software that collects data and also the vehicle or robot. This is a dynamic process with a virtually unlimited variability.

As the robot moves it adds scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method known as scan matching. This assists in establishing loop closures. The SLAM algorithm is updated with its robot's estimated trajectory when a loop closure has been identified.

The fact that the environment changes in time is another issue that makes it more difficult for SLAM. For instance, if a robot is walking through an empty aisle at one point and then comes across pallets at the next location it will be unable to connecting these two points in its map. This is where handling dynamics becomes important and is a typical characteristic of the modern Lidar SLAM algorithms.

Despite these challenges however, a properly designed SLAM system can be extremely effective for navigation and 3D scanning. It is particularly beneficial in environments that don't allow the robot to rely on GNSS-based positioning, such as an indoor factory floor. It is important to note that even a well-designed SLAM system can be prone to errors. It is essential to be able to detect these issues and comprehend how they impact the SLAM process in order to correct them.

Mapping

The mapping function creates a map of a robot's surroundings. This includes the robot as well as its wheels, actuators and everything else within its vision field. The map is used to perform the localization, planning of paths and obstacle detection. This is an area in which 3D lidars can be extremely useful since they can be effectively treated as an actual 3D camera (with only one scan plane).

The process of building maps takes a bit of time however, the end result pays off. The ability to create an accurate and complete map of the robot's surroundings allows it to move with high precision, as well as over obstacles.

In general, the higher the resolution of the sensor then the more precise will be the map. However there are exceptions to the requirement for high-resolution maps. For example, a floor sweeper may not require the same level of detail as an industrial robot navigating factories of immense size.

For this reason, there are a number of different mapping algorithms to use with lidar product sensors. One of the most well-known algorithms is Cartographer which utilizes two-phase pose graph optimization technique to adjust for drift and keep an accurate global map. It is particularly efficient when combined with odometry data.

Another alternative is GraphSLAM that employs a system of linear equations to model the constraints of graph. The constraints are modeled as an O matrix and an the X vector, with every vertice of the O matrix representing a distance to a landmark on the X vector. 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 account for new robot observations.

Another helpful mapping algorithm is SLAM+, which combines the use of 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 in the features mapped by the sensor. The mapping function is able to make use of this information to better estimate its own location, allowing it to update the base map.

Obstacle Detection

A robot should 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. Additionally, it utilizes inertial sensors that measure its speed and position, as well as its orientation. These sensors help it navigate 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 attached to the vehicle, the robot or even a pole. It is crucial to keep in mind that the sensor could be affected by a variety of factors, such as wind, rain, and fog. It is essential to calibrate the sensors prior each use.

A crucial step in obstacle detection is to identify static obstacles, which can be accomplished 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 the laser lines and the camera's angular velocity. To overcome this issue multi-frame fusion was implemented to increase the accuracy of static obstacle detection.

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

The results of the experiment showed that the algorithm could accurately determine the height and location of obstacles as well as its tilt and rotation. It also had a great ability to determine the size of the obstacle and its color. The method also demonstrated excellent stability and durability even when faced with moving obstacles.