LiDAR Robot Navigation

LiDAR robots navigate by using a combination of localization, mapping, and also path planning. This article will explain the concepts and show how they work using a simple example where the robot is able to reach an objective within a plant row.

LiDAR sensors are low-power devices that can prolong the life of batteries on robots and decrease the amount of raw data needed for 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 emits laser pulses into the surrounding. These light pulses bounce off the surrounding objects at different angles depending on their composition. The sensor measures the time it takes for each return and uses this information to calculate distances. The sensor is typically mounted on a rotating platform, permitting it to scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on their intended applications in the air or on land. Airborne lidars are usually attached to helicopters or unmanned aerial vehicle (UAV). Terrestrial lidar robot vacuums systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is recorded by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by LiDAR systems to determine the precise location of the sensor within the space and time. The information gathered is used to create a 3D representation of the surrounding environment.

LiDAR scanners are also able to identify different kinds of surfaces, which is especially useful when mapping environments with dense vegetation. When a pulse passes a forest canopy, it will typically register multiple returns. The first one is typically associated with the tops of the trees, while the last is attributed with the ground's surface. If the sensor captures these pulses separately, it is called discrete-return LiDAR.

Discrete return scans can be used to study surface structure. For instance, a forest area could yield a sequence of 1st, 2nd and 3rd return, with a final, large pulse representing the bare ground. The ability to separate and store these returns in a point-cloud allows for detailed models of terrain.

Once a 3D model of environment is constructed, the robot will be able to use this data to navigate. This involves localization and making a path that will reach a navigation "goal." It also involves dynamic obstacle detection. This is the process of identifying new obstacles that aren't present on the original map and adjusting the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment, and then identify its location relative to that map. Engineers use the information to perform a variety of purposes, including the planning of routes and obstacle detection.

To utilize SLAM the robot needs to have a sensor that gives range data (e.g. A computer with the appropriate software for processing the data and a camera or a laser are required. Also, you will require 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 process is a complex one, and many different back-end solutions are available. Whatever option you select for the success of SLAM, it requires constant interaction between the range measurement device and the software that collects data and the robot or vehicle. This is a highly dynamic process that has an almost infinite amount of variability.

When the robot vacuum with lidar and camera moves, it adds scans to its map. The SLAM algorithm compares these scans with prior ones using a process called 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 factor that complicates SLAM is the fact that the surrounding changes as time passes. If, for example, your robot is walking along an aisle that is empty at one point, but it comes across a stack of pallets at another point it might have trouble finding the two points on its map. This is where handling dynamics becomes crucial, and this is a standard feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient in 3D scanning and navigation despite these challenges. It is especially beneficial in situations that don't rely on GNSS for positioning for example, an indoor factory floor. It is important to keep in mind that even a properly-configured SLAM system could be affected by errors. To fix these issues, it is important to be able to recognize them and comprehend their impact on the SLAM process.

Mapping

The mapping function builds a map of the robot's surrounding, which includes the robot as well as its wheels and actuators and everything else that is in the area of view. This map is used for the localization, planning of paths and obstacle detection. This is an area in which 3D Lidars can be extremely useful, since they can be used as a 3D Camera (with one scanning plane).

Map creation is a long-winded process but it pays off in the end. The ability to create a complete, consistent map of the robot's environment allows it to carry out high-precision navigation, as well as navigate around obstacles.

The higher the resolution of the sensor then the more precise will be the map. However, not all robots need high-resolution maps: for example, a floor sweeper may not need the same amount of detail as a industrial robot that navigates factories with huge facilities.

To this end, there are a variety of different mapping algorithms for use with Lidar Vacuum sensors. Cartographer is a popular algorithm that employs a two phase pose graph optimization technique. It corrects for drift while ensuring a consistent global map. It is particularly useful when paired with odometry data.

Another alternative is GraphSLAM that employs a system of linear equations to model constraints in graph. The constraints are represented as an O matrix, and a X-vector. Each vertice in the O matrix is a distance from an X-vector landmark. A GraphSLAM update is the addition and subtraction operations on these matrix elements, and the result is that all of the O and X vectors are updated to account for new information about the robot.

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

Obstacle Detection

A robot should be able to see its surroundings so that it can overcome obstacles and reach its destination. It utilizes sensors such as digital cameras, infrared scanners, sonar and laser radar to determine its surroundings. Additionally, it utilizes inertial sensors to measure its speed and position as well as its orientation. These sensors help it navigate in a safe manner and avoid collisions.

One important part of this process is the detection of obstacles that involves the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be attached to the vehicle, the robot or even a pole. It is important to keep in mind that the sensor can be affected by many elements, including rain, wind, and fog. Therefore, it is important to calibrate the sensor lidar Vacuum prior to every use.

The most important aspect of obstacle detection is identifying static obstacles, which can be done by using the results of the eight-neighbor-cell clustering algorithm. This method is not very accurate because of the occlusion induced by the distance between the laser lines and the camera's angular speed. To address this issue multi-frame fusion was implemented to increase the accuracy of the static obstacle detection.

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

The results of the test revealed that the algorithm was able to accurately identify the height and location of obstacles as well as its tilt and rotation. It was also able to detect the size and color of the object. The method also demonstrated solid stability and reliability even when faced with moving obstacles.