LiDAR Robot Navigation

LiDAR robots navigate using a combination of localization and mapping, as well as path planning. This article will present these concepts and show how they interact using an example of a robot achieving a goal within a row of crops.

LiDAR sensors have low power requirements, allowing them to increase the life of a robot's battery and decrease the need for raw data for localization algorithms. This allows for a greater number of iterations of the SLAM algorithm without overheating the GPU.

lidar robot navigation Sensors

The central component of a lidar system is its sensor that emits pulsed laser light into the environment. These light pulses bounce off objects around them at different angles based on their composition. The sensor measures the amount of time required for each return and then uses it to calculate distances. The sensor is typically mounted on a rotating platform allowing it to quickly 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 lidar systems are commonly connected to aircrafts, helicopters, or UAVs. (UAVs). Terrestrial LiDAR is 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 usually captured by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to determine the precise position of the sensor within the space and time. This information is then used to create a 3D model of the environment.

LiDAR scanners can also be used to detect different types of surface, which is particularly useful when mapping environments that have dense vegetation. For instance, when an incoming pulse is reflected through a forest canopy, it is common for it to register multiple returns. The first return is attributable to the top of the trees, and the last one is associated with the ground surface. If the sensor records these pulses separately this is known as discrete-return LiDAR.

Discrete return scans can be used to determine surface structure. For instance, a forest region may yield an array of 1st and 2nd returns, with the last one representing bare ground. The ability to separate and store these returns as a point-cloud allows for precise models of terrain.

Once a 3D map of the surrounding area has been built, the robot can begin to navigate based on this data. This process involves localization, constructing an appropriate path to reach a goal for navigation and dynamic obstacle detection. The latter is the process of identifying obstacles that are not present on the original map 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 determine its location relative to that map. Engineers utilize this information for a range of tasks, such as path planning and obstacle detection.

To use SLAM your robot has to have a sensor that provides range data (e.g. a camera or laser) and a computer that has the appropriate software to process the data. You also need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that will precisely track the position of your robot in an unknown environment.

The SLAM process is complex and a variety of back-end solutions exist. Whatever solution you choose for an effective SLAM is that it requires a constant interaction between the range measurement device and the software that extracts data and also the robot or vehicle. It is a dynamic process that is almost indestructible.

When the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method called scan matching. This aids in establishing loop closures. When a loop closure is discovered when loop closure is detected, the SLAM algorithm makes use of this information to update its estimated robot trajectory.

The fact that the surrounding changes over time is a further factor that can make it difficult to use SLAM. For example, if your robot walks through an empty aisle at one point, and then comes across pallets at the next point it will be unable to matching these two points in its map. Handling dynamics are important in this case and are a characteristic of many modern Lidar SLAM algorithms.

Despite these issues, a properly-designed SLAM system can be extremely effective for navigation and 3D scanning. It is particularly useful in environments that don't let the robot rely on GNSS positioning, like an indoor factory floor. It's important to remember that even a well-designed SLAM system may experience errors. It is crucial to be able to spot these flaws and understand how they affect the SLAM process in order to fix them.

Mapping

The mapping function creates an image of the robot's surrounding that includes the robot including its wheels and actuators as well as everything else within its view. The map is used for the localization, planning of paths and obstacle detection. This is an area in which 3D lidars are particularly helpful since they can be effectively treated like an actual 3D camera (with only one scan plane).

The map building process can take some time however the results pay off. The ability to build a complete and consistent map of the robot's surroundings allows it to navigate with great precision, and also around obstacles.

As a general rule of thumb, the higher resolution the sensor, more precise the map will be. However, not all robots need high-resolution maps. For example floor sweepers might not require the same level of detail as an industrial robot navigating factories of immense size.

This is why there are many different mapping algorithms to use with LiDAR 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 used in conjunction with the odometry.

GraphSLAM is another option, which uses a set of linear equations to model the constraints in diagrams. The constraints are represented 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 is a series of additions and subtraction operations on these matrix elements with the end result being that all of the O and X vectors are updated to accommodate new information about the robot.

Another efficient mapping algorithm is SLAM+, which combines mapping and odometry 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 recorded by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location, and LiDAR Robot Navigation also to update the map.

Obstacle Detection

A robot needs to be able to see its surroundings so that it can overcome obstacles and reach its goal. It makes use of sensors like digital cameras, infrared scans laser radar, and sonar to detect the environment. It also makes use of an inertial sensor to measure its position, speed and orientation. These sensors help it navigate in a safe manner 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 robot, a vehicle or a pole. It is important to remember that the sensor could be affected by many factors, such as wind, rain, and fog. It is essential to calibrate the sensors prior to each use.

A crucial step in obstacle detection is to identify static obstacles. This can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However this method has a low detection accuracy due to the occlusion created by the spacing between different laser lines and the angle of the camera making it difficult to detect static obstacles in one frame. To address this issue multi-frame fusion was implemented to increase the accuracy of static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for further navigational tasks, like path planning. This method provides an accurate, high-quality image of the environment. The method has been compared with other obstacle detection methods including YOLOv5, VIDAR, and monocular ranging in outdoor comparative tests.

The results of the experiment showed that the algorithm could correctly identify the height and location of an obstacle, as well as its tilt and rotation. It was also able identify the color and size of the object. The method also exhibited solid stability and reliability, even when faced with moving obstacles.