Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection

LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

Section: Related Workmentioning

confidence: 99%

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Cho

Kim

Park

et al. 2020

“…Yet, it still has shortcomings, such as higher cost and lower resolution. LiDAR-SLAM [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] uses environmental features extracted from the point cloud to match and obtain pose changes for navigation. Similar to INS, LiDAR-SLAM is also a type of recursive navigation and its navigation error gradually diverges with the moving distance.…”

Section: Introductionmentioning

confidence: 99%

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

Chang

Niu

Liu

2020

In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.

“…The iterative closest point (ICP) method is a well-known scan-matching and registration algorithm [29] that was proposed for point-to-point registration [30] and point-to-surface registration [31] in the 1990s to minimize the differences between two point clouds and to match them as closely as possible. This algorithm is robust and straightforward [32]; however, it has some problems in real-time applications such as SLAM due to heavy computation burden [33,34] and huge execution time [35]. Also, sparse point clouds and high-speed moving platforms introducing motion distortion can affect the performance of this algorithm negatively [36].…”

Section: Introductionmentioning

confidence: 99%

“…The matching time of the NDT is better than ICP because NDT does not require point-to-point registration [34]. However, the determination of the grid size is a critical step in this algorithm, which is an issue for inhomogeneous point clouds [41] that dominate the estimation stability and determines the performance of the algorithm [35]. This algorithm has been used for many applications, such as path planning, change detection, and loop detection [32].…”

Section: Introductionmentioning

confidence: 99%

“…The LiDAR odometry carries out course processing to determine the velocity at a higher frequency, and LiDAR mapping performs fine processing to create maps at a lower frequency [43]. Increasing drift error over time that is not corrected is the main drawback of this algorithm because of no loop closure detection [35]. Particularly, the performance of this algorithm severely degrades if the number of moving objects increases, such as in urban areas [42].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.