LiDAR Robot Navigation

LiDAR robot navigation is a sophisticated combination of localization, mapping, and path planning. This article will explain these concepts and show how they interact using an easy example of the robot achieving a goal within a row of crop.

LiDAR sensors are low-power devices that can prolong 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 the GPU.

LiDAR Sensors

The central component of lidar systems is their sensor that emits laser light pulses into the environment. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor measures how long it takes for each pulse to return and then uses that information to determine distances. Sensors are placed on rotating platforms that allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified based on whether they're intended for use in the air or on the ground. Airborne lidar systems are commonly connected to aircrafts, helicopters, or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually mounted on a robot platform that is stationary.

To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is usually gathered by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems in order to determine the precise position of the sensor within space and time. This information is used to create a 3D model of the surrounding.

LiDAR scanners can also be used to recognize different types of surfaces and types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. For instance, when a pulse passes through a forest canopy it will typically register several returns. The first one is typically associated with the tops of the trees while the second is associated with the ground's surface. If the sensor records each peak of these pulses as distinct, it is called discrete return LiDAR.

Discrete return scans can be used to study surface structure. For example, a forest region may result in a series of 1st and 2nd returns with the final big pulse representing bare ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of precise terrain models.

Once a 3D model of the surroundings is created and the robot has begun to navigate based on this data. This involves localization and building a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that aren't visible on the original map and updating the path 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 make use of this information for a variety of tasks, including path planning and obstacle detection.

For SLAM to function the robot needs a sensor (e.g. a camera or laser), and a computer running the appropriate software to process the data. You will also require an inertial measurement unit (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 there are a variety of back-end options. No matter which solution you choose for an effective SLAM it requires constant communication between the range measurement device and the software that extracts the data, as well as the vehicle or robot. This is a highly dynamic procedure that has an almost infinite amount of variability.

As the robot moves around and around, it adds new scans to its map. The SLAM algorithm then compares these scans to previous ones using a process called scan matching. This helps to establish loop closures. The SLAM algorithm adjusts its estimated robot trajectory when the loop has been closed identified.

The fact that the surroundings changes in time is another issue that complicates SLAM. For instance, if your robot is walking along an aisle that is empty at one point, but it comes across a stack of pallets at a different location it might have trouble connecting the two points on its map. This is when handling dynamics becomes critical, and this is a typical feature of the modern Lidar SLAM algorithms.

Despite these difficulties, a properly-designed SLAM system can be extremely effective for navigation and 3D scanning. It is particularly beneficial in situations where the robot can't rely on GNSS for positioning for positioning, like an indoor factory floor. However, it's important to keep in mind that even a properly configured SLAM system can experience errors. To correct these errors it is crucial to be able to recognize them and understand their impact on the SLAM process.

Mapping

The mapping function creates an image of the robot's environment, which includes the robot itself, its wheels and actuators as well as everything else within the area of view. This map is used for localization, path planning, and obstacle detection. This is an area where 3D lidars are extremely helpful because they can be effectively treated as a 3D camera (with one scan plane).

Map creation is a long-winded process however, it is worth it in the end. The ability to create a complete and consistent map of the environment around a robot allows it to move with high precision, and also over obstacles.

As a rule of thumb, the greater resolution the sensor, the more precise the map will be. However it is not necessary for all robots to have high-resolution maps: for LiDAR Robot Navigation example, a floor sweeper may not need the same degree of detail as an industrial robot vacuum with lidar and camera 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 utilizes a two-phase pose graph optimization technique to adjust for drift and keep an accurate global map. It is particularly useful when paired with Odometry.

Another alternative is GraphSLAM that employs linear equations to model the constraints in graph. The constraints are modelled as an O matrix and a X vector, with each vertice of the O matrix representing the distance to a landmark on the X vector. A GraphSLAM Update is a series of additions and subtractions on these matrix elements. The end result is that all the O and X Vectors are updated to take into account the latest observations made by the robot.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current location, but also the uncertainty of the features that were recorded by the sensor. The mapping function can then make use of this information to estimate its own position, which allows it to update the base map.

Obstacle Detection

A robot must be able to see its surroundings in order to avoid obstacles and reach its final point. It employs sensors such as digital cameras, infrared scans, sonar and laser radar to determine the surrounding. It also utilizes an inertial sensors to determine its speed, location and its orientation. These sensors allow it to navigate safely and avoid collisions.

One of the most important aspects of this process is obstacle detection that involves 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 crucial to keep in mind that the sensor may be affected by many elements, LiDAR Robot Navigation including wind, rain, and fog. Therefore, it is essential to calibrate the sensor prior to every use.

A crucial step in obstacle detection is the identification of static obstacles, which can be done by using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low accuracy in detecting because of the occlusion caused by the gap between the laser lines and the angle of the camera, which makes it difficult to detect static obstacles in one frame. To overcome this problem, a method of multi-frame fusion was developed to increase the detection accuracy of static obstacles.

The method of combining roadside camera-based obstruction detection with a vehicle camera has been proven to increase the efficiency of data processing. It also provides the possibility of redundancy for other navigational operations, like path planning. The result of this technique is a high-quality picture of the surrounding environment that is more reliable than one frame. The method has been compared with other obstacle detection techniques including YOLOv5, VIDAR, and monocular ranging, in outdoor comparative tests.

The experiment results revealed that the algorithm was able to accurately identify the height and position of an obstacle, as well as its tilt and rotation. It also had a good performance in identifying the size of the obstacle and its color. The method also showed solid stability and reliability, even when faced with moving obstacles.