Five Lidar Robot Navigation Lessons From The Pros

LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of mapping, localization and path planning. This article will explain the concepts and explain how they work using an example in which the robot achieves the desired goal within the space of a row of plants.

LiDAR sensors are low-power devices that extend the battery life of robots and reduce the amount of raw data needed to run localization algorithms. This allows for more iterations of SLAM without overheating GPU.

LiDAR Sensors

The sensor is at the center of the Lidar system. It emits laser beams into the surrounding. These light pulses bounce off objects around them at different angles depending on their composition. The sensor determines how long it takes for each pulse to return and uses that information to determine distances. Sensors are placed on rotating platforms, which allows them to scan the area around them quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified by their intended airborne or terrestrial application. Airborne lidar systems are usually mounted on aircrafts, helicopters or UAVs. (UAVs). Terrestrial LiDAR systems are generally mounted on a stationary robot platform.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually gathered through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to calculate the precise position of the sensor within space and time. This information is then used to create a 3D representation of the surrounding environment.

LiDAR scanners can also detect different kinds of surfaces, which is especially useful when mapping environments with dense vegetation. For instance, when the pulse travels through a forest canopy it is likely to register multiple returns. Typically, the first return is associated with the top of the trees, while the final return is attributed to the ground surface. If the sensor records these pulses separately this is known as discrete-return LiDAR.

The use of Discrete Return scanning can be useful in studying surface structure. For instance, a forest region may yield a series of 1st and 2nd returns, with the final large pulse representing the ground. The ability to divide these returns and save them as a point cloud allows for the creation of detailed terrain models.

Once a 3D map of the surrounding area has been built, the robot can begin to navigate using this information. This process involves localization, constructing a path to reach a navigation 'goal,' and dynamic obstacle detection. This is the process that identifies new obstacles not included in the original map and then updates the plan of travel accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and LiDAR robot navigation localization) is an algorithm which allows your robot to map its surroundings, and then determine its position relative to that map. Engineers use this information for a variety of tasks, including the planning of routes and obstacle detection.

To utilize SLAM your robot has to be equipped with a sensor that can provide range data (e.g. a camera or laser), and a computer running the right software to process the data. You will also require an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can accurately determine the location of your robot in a hazy environment.

The SLAM process is a complex one, and many different back-end solutions exist. Whatever solution you choose, a successful SLAM system requires a constant interplay between the range measurement device and the software that extracts the data and the vehicle or robot. This is a highly dynamic process that is prone to an infinite amount of variability.

As the robot moves the area, it adds new scans to its map. The SLAM algorithm compares these scans to prior ones using a process known as scan matching. This assists in establishing loop closures. When a loop closure is discovered, the SLAM algorithm makes use of this information to update its estimated robot trajectory.

The fact that the surroundings changes over time is another factor that complicates SLAM. For example, if your robot is walking through an empty aisle at one point and LiDAR Robot Navigation then encounters stacks of pallets at the next spot, it will have difficulty matching these two points in its map. This is where handling dynamics becomes important and is a common characteristic of the modern Lidar SLAM algorithms.

Despite these issues, a properly-designed SLAM system can be extremely effective for navigation and 3D scanning. It is especially useful in environments where the robot isn't able to rely on GNSS for positioning for example, an indoor factory floor. However, it is important to note that even a properly configured SLAM system may have errors. It is essential to be able to spot these issues and comprehend how they impact the SLAM process in order to fix them.

Mapping

The mapping function creates a map for a robot's surroundings. This includes the robot as well as its wheels, actuators and everything else that falls within its field of vision. This map is used for the localization, planning of paths and obstacle detection. This is an area where 3D lidars can be extremely useful since they can be utilized like a 3D camera (with one scan plane).

The process of creating maps takes a bit of time, but the results pay off. The ability to build an accurate and complete map of the robot vacuums with lidar's surroundings allows it to navigate with great precision, as well as around obstacles.

As a rule of thumb, the higher resolution the sensor, the more accurate the map will be. Not all robots require maps with high resolution. For example a floor-sweeping robot might not require the same level of detail as a robotic system for industrial use navigating large factories.

For this reason, there are a variety of different mapping algorithms to use with LiDAR sensors. One popular algorithm is called Cartographer which utilizes a two-phase pose graph optimization technique to correct for drift and maintain an accurate global map. It is particularly useful when paired with Odometry.

Another alternative is GraphSLAM which employs linear equations to model the constraints of a graph. The constraints are modeled as an O matrix and an one-dimensional X vector, each vertex of the O matrix representing the distance to a point on the X vector. A GraphSLAM Update is a series of subtractions and additions to these matrix elements. The result is that both the O and X Vectors are updated to reflect 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 changes the uncertainty of the robot's position as well as the uncertainty of the features recorded by the sensor. The mapping function is able to utilize this information to estimate its own position, allowing it to update the underlying map.

Obstacle Detection

A robot must be able detect its surroundings to avoid obstacles and reach its destination. It employs sensors such as digital cameras, infrared scans, sonar and laser radar to sense the surroundings. In addition, it uses inertial sensors to measure its speed, position and orientation. These sensors assist it in navigating in a safe way and prevent collisions.

A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be mounted on the robot, inside the vehicle, or on a pole. It is crucial to keep in mind that the sensor is affected by a myriad of factors such as wind, rain and fog. It is important to calibrate the sensors prior every use.

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

The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to increase the data processing efficiency and reserve redundancy for further navigational operations, like path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than one frame. The method has been tested against other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging, in outdoor tests of comparison.

The experiment results showed that the algorithm could accurately identify the height and location of an obstacle, as well as its tilt and rotation. It also had a great performance in detecting the size of obstacles and its color. The algorithm was also durable and stable, even when obstacles moved.