Abstract
Graph-based simultaneous localization and mapping (SLAM) is one of the methods to generate point cloud maps which are used for various applications in autonomous vehicles. Graph-based SLAM represents the pose of the vehicle as a node and the odometry between two different nodes as an edge. Among the edge generating methods, scan matching, light detection and ranging (LiDAR) based method, can provide an accurate pose between two nodes based on the high distance accuracy of the LiDAR. However, the point cloud in real driving situations contains numerous moving objects, which degrade the scan-matching performance. Therefore, this article defines the static probability which means the likelihood that an acquired point is from a static object, and proposes the weighted normal distribution transformation (NDT), which is achieved by modifying NDT. Weighted NDT is a scan-matching algorithm which can reflect the static probability of each point as a weight. The odometry from the weighted NDT is utilized for graph construction to generate a robust point cloud map even in a dynamic environment. Finally, the proposed algorithm was compared with the existing object removal algorithms in two areas: dynamic object classification and scan-matching performance. Based on the scan-matching results, the accuracy of the point cloud map generated by the proposed algorithm was evaluated with a reference map using high-performance global navigation satellite system (GNSS). It was confirmed that the proposed algorithm has higher classification accuracy and lower scan-matching error compared with other dynamic object removal methods. The proposed algorithm was able to generate a point cloud map, despite the presence of many dynamic objects, that was similar to a map generated in the absence of dynamic objects in the same environment.
Highlights
Light detection and ranging (LiDAR), which is an essential sensor in autonomous vehicles, has the following twoThe associate editor coordinating the review of this manuscript and approving it for publication was Razi Iqbal .characteristics
This can result in incorrect estimates of the relative pose while the point clouds are aligned owing to the effect of the dynamic objects [12]
To reduce the effect of dynamic points, we estimate the static probability of individual points and assign different weights based on this probability in the scan-matching process
Summary
Light detection and ranging (LiDAR), which is an essential sensor in autonomous vehicles, has the following two. LiDAR provides accurate distance information on objects and detailed depictions of the surrounding environment at a high resolution These characteristics of LiDAR can be used in scan-matching to match the same static object in two different point clouds. If only static objects exist in the environment where point cloud maps are created, scan-matching can accurately estimate the relative pose. In a real driving environment, there are numerous dynamic objects such as cars and pedestrians, which change their pose during the data acquisition for mapping This can result in incorrect estimates of the relative pose while the point clouds are aligned owing to the effect of the dynamic objects [12]. The modified NDT algorithm reflects the calculated static probability to exclude the influence of dynamic points This method can estimate the related poses of two-point clouds accurately in a dynamic environment. An accurate point cloud map in a dynamic environment is generated based on the accurate relative pose
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
