LiDAR Robot Navigation

lidar vacuum robots navigate by using the combination of localization and mapping, and also path planning. This article will explain these concepts and demonstrate how they interact using an easy example of the eufy L60 Hybrid Robot Vacuum Self Empty achieving a goal within a row of crops.

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

LiDAR Sensors

The core of a lidar system is its sensor that emits laser light pulses into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor measures the amount of time required to return each time and uses this information to calculate distances. Sensors are positioned on rotating platforms, which allow them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified according to whether they're intended for airborne application or terrestrial application. Airborne lidars are typically connected to helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR is typically installed on a robotic platform that is stationary.

To accurately measure distances, the sensor needs to be aware of the precise location of the robot at all times. This information is recorded by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are utilized by LiDAR systems to calculate the precise location of the sensor in space and time. This information is then used to create a 3D representation of the surrounding.

LiDAR scanners can also identify different types of surfaces, which is especially useful when mapping environments that have dense vegetation. When a pulse passes a forest canopy it will usually generate multiple returns. Typically, the first return is attributed to the top of the trees, and the last one is attributed to the ground surface. If the sensor records these pulses in a separate way, it is called discrete-return LiDAR.

Discrete return scans can be used to analyze the structure of surfaces. For instance the forest may produce one or two 1st and 2nd returns, with the final big pulse representing bare ground. The ability to separate and store these returns as a point-cloud permits detailed models of terrain.

Once a 3D model of the environment has been built, the robot can begin to navigate using this data. This process involves localization and making a path that will reach a navigation "goal." It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that aren't present on the original map and then updating the plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create a map of its environment and then determine the location of its position relative to the map. Engineers make use of this information to perform a variety of purposes, including path planning and obstacle identification.

To utilize SLAM the robot needs to have a sensor that provides range data (e.g. A computer with the appropriate software to process the data as well as cameras or lasers are required. Also, you 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 a hazy environment.

The SLAM system is complicated and there are many different back-end options. Regardless of which solution you choose for your SLAM system, a successful SLAM system requires a constant interaction between the range measurement device, the software that extracts the data, and the robot or vehicle itself. This is a highly dynamic procedure that has an almost infinite amount of variability.

As the robot moves around, it adds new scans to its map. The SLAM algorithm compares these scans to the previous ones using a process known as scan matching. This allows loop closures to be established. The SLAM algorithm updates its estimated robot trajectory once loop closures are discovered.

The fact that the environment changes over time is a further factor that makes it more difficult for SLAM. If, for instance, your robot is walking down an aisle that is empty at one point, and then comes across a pile of pallets at a different point, it may have difficulty finding the two points on its map. This is where handling dynamics becomes crucial, and this is a common feature of the modern Lidar SLAM algorithms.

Despite these difficulties, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially beneficial in situations that don't rely on GNSS for its positioning for example, an indoor factory floor. It is important to keep in mind that even a well-configured SLAM system can experience mistakes. To correct these errors it is essential to be able to recognize them and comprehend their impact on the SLAM process.

Mapping

The mapping function creates a map of the robot's surroundings. This includes the robot, its wheels, actuators and everything else within its vision field. This map is used for localization, path planning and obstacle detection. This is a field in which 3D Lidars can be extremely useful as they can be treated as a 3D Camera (with a single scanning plane).

The map building process may take a while however the results pay off. The ability to build a complete and consistent map of the robot's surroundings allows it to navigate with high precision, as well as around obstacles.

As a rule of thumb, the higher resolution of the sensor, the more accurate the map will be. However there are exceptions to the requirement for high-resolution maps. For example floor sweepers might not require the same degree of detail as an industrial robot that is navigating factories with huge facilities.

There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a well-known algorithm that employs a two phase pose graph optimization technique. It corrects for drift while maintaining an accurate global map. It is particularly beneficial when used in conjunction with Odometry data.

Another alternative is GraphSLAM, which uses a system of linear equations to model the constraints in graph. The constraints are represented as an O matrix and an one-dimensional X vector, each vertex of the O matrix representing the 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 location, but also the uncertainty in the features that were recorded by the sensor. This information can be used by the mapping function to improve its own estimation of its location and to update the map.

Obstacle Detection

A robot must be able perceive its environment so that it can avoid obstacles and reach its goal. It uses sensors like digital cameras, infrared scanners sonar and laser radar to sense its surroundings. It also makes use of an inertial sensors to determine its position, speed and orientation. These sensors aid in navigation in a safe and secure manner and avoid collisions.

A range sensor is used to gauge the distance between an obstacle and a robot. The sensor can be mounted to the vehicle, the robot, or a pole. It is crucial to keep in mind that the sensor is affected by a variety of elements such as wind, rain and fog. Therefore, it is essential to calibrate the sensor prior each use.

The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. However this method has a low detection accuracy due to the occlusion caused by the spacing between different laser lines and the angle of the camera, which makes it difficult to identify static obstacles in one frame. To overcome this issue multi-frame fusion was employed to improve the accuracy of static obstacle detection.

The method of combining roadside camera-based obstruction detection with a vehicle camera has proven to increase the efficiency of data processing. It also provides redundancy for other navigation operations, LiDAR Robot Navigation like path planning. This method creates an accurate, high-quality image of the surrounding. In outdoor comparison tests the method was compared to other methods for detecting obstacles such as YOLOv5 monocular ranging, and VIDAR.

The results of the test proved that the algorithm was able to correctly identify the height and location of an obstacle, as well as its tilt and rotation. It was also able to determine the size and color of an object. The method was also reliable and stable even when obstacles moved.