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3D LiDAR SLAM Software and Hardware Technology

Jan 05, 2023      View: 384

SLAM, simultaneous localization and mapping, called instantaneous localization and map construction, is mainly useful for self-driving vehicles or robots to start from unknown locations in unknown environments, locate their positions and postures by repeatedly observing map features during the movement, and then incrementally construct maps according to their positions, to achieve The important theoretical and application value of SLAM is considered as a key technology for realizing autonomous driving and fully autonomous mobile robots.

The rapid popularity of unmanned devices has accelerated the development of the SLAM industry, and to take advantage of these opportunities, technology providers are integrating SLAM advanced 3D technologies for industry layout.

From 3D LIDAR to market, innovate 2D LIDAR SLAM.

Roughly speaking, two types of technologies are needed to realize SLAM. One type of technology is sensor signal processing (including front-end processing), which depends heavily on the sensors used. The other type of technology is bitmap optimization (including back-end processing), which is sensor-independent. So although SLAM is an algorithmic technique, the basis for applying SLAM is a high-performance sensor (LIDAR or image sensor). Depending on the choice of sensor, there are two schools of technology: visual V-SLAM and LIDAR SLAM.

LiDAR SLAM technology currently has two types of technology: 2D laser SLAM and 3D laser SLAM technology. 2D laser SLAM is limited by sensor performance, can only recognize 2D planes, and cannot be applied to complex 3D environments. 3D SLAM positioning technology based on multi-line LiDAR is the world's leading 3D SLAM positioning and navigation technology. 3D SLAM positioning technology is based on 3D LiDAR as the main sensor, through the data fusion processing of various sensors such as LiDAR, IMU, odometer, GPS, etc., and configured with high-performance processors to realize the map building and matching positioning function of the unmanned vehicle environment.

3D LIDAR SLAM combined with the supporting Ethernet communication development interface can enable various application scenarios of low-speed uncrewed vehicles, robots, unmanned forklifts, AGVs and other low-speed intelligent mobile carriers to achieve precise positioning requirements in complex 3D environments.

The combination of software and hardware highlights the advantages of SLAM technology.

Compared with most manufacturers that can only cut into the market from the laser SLAM algorithm, sensor manufacturers do their SLAM advantage is often greater, so they cannot only provide navigation algorithms and high-performance hardware to match. Currently, many vision sensor manufacturers and LiDAR manufacturers self-research SLAM, a collection of sensors for midstream manufacturers, to provide the overall program to do differentiated competition. Software and hardware manufacturers will have more advantages in SLAM technology.

SLAM technology is most intuitively reflected in the point cloud building map, generally speaking, firstly, the movement is continuously estimated by point cloud matching. The calculated movement data (distance moved) is then used to localize the vehicle. For laser point cloud matching, alignment algorithms such as iterative nearest point and normal distribution transformation are used. The 2D or 3D point cloud maps can be represented as raster maps or voxel maps. Multi-line LIDAR, 3D SLAM technology, can theoretically build 3D point cloud maps of mega scenes of millions of square meters with rich perceived environmental information features and stable localization matching for most scenes. Why do I say theoretically? Because the premise is that the sensor performance is good enough to generate dense point clouds.

From the sensor point of view, the laser point cloud is not as fine as the image's density. It cannot guarantee enough features for matching, which is why it will additionally carry IMU, odometer, GPS and other kinds of sensors for data fusion processing.

The main control chip is gradually shifting from FPGA and MCU to LiDAR manufacturers' self-research SoC to SoC + SLAM algorithm packaged to downstream manufacturers, giving the manufacturers more advantages in cost control.


The debate about the two schools of LIDAR SLAM and V-SLAM has been going on for many years, and the fans of each technology line believe that each adheres to the technology line with irreplaceable advantages. However, the undeniable fact is that V-SLAM, despite the hot topic, has not landed as many vision solutions as laser SLAM in real applications.

Taking robotics companies as an example, most of them adopt multiple types of hybrid navigation technology, such as vision sensing with laser SLAM, to work to complement each other's advantages. Still, the actual role of vision SLAM in hybrid navigation is difficult to test.

From the software algorithm and hardware level perspective, LIDAR 3D SLAM is more mature than V-SLAM. LIDAR 3D SLAM is more adaptable to the environment, not affected by light, and can be used indoors and outdoors, so it has the basis for large-scale application. On the other hand, V-SLAM relies on a camera, which is limited by the visual sensor's interference with ambient light and cannot be used in daytime or dark environments at night. It is also difficult to extract environmental features.

Lidar 3D SLAM technology is bound to be the future development trend, most scenes, the multi-line laser slam fusion RTk IMU odometer is sufficient, plus visually rather cumbersome. Previously there will be V-SLAM technology direction because of the low cost of the camera, but with the optimization of the cost of LiDAR and the price gradually down, as well as more mature algorithms and applications, V-SLAM cost advantage will not be so obvious.

Final Words

The rapid popularity of unmanned devices has driven the rapid growth of the SLAM market, and the rise of 3D SLAM has become an important development trend of global SLAM technology, and 3D LiDAR SLAM, with its cost, constantly sinking, is rapidly becoming the mainstream direction of positioning and navigation. With the significant increase in computer processing speed and the application of lower-cost sensors, this technology is widely used in various fields, from AR to robotics to autonomous driving and updating 2D SLAM technology-related applications.

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