2P2-C14 The generation of Environmental Map based on a High Resolutional NDT Grid Mapping

Kaminade, Takuya; Takubo, Tomohito; Mae, Yasushi; Arai, Tatsuo

doi:10.1299/jsmermd.2008._2p2-c14_1

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This log odds ratio can be accumulated as the sum of individual log odds ratios by the binary Bayes' filter in Equation (10).…”

Section: Final Static Probability Estimation By Integrating the Stmentioning

confidence: 99%

“…In general, an inertial navigation system (INS) or environment-aware sensor (e.g., camera, LiDAR) is used to generate edge constraints [7], [8]. Among the various methods for constructing graph edge constraints, scan-matching can be used in graph-based SLAM for LiDAR-based generation [9], [10], [11]. LiDAR provides accurate distance information on objects and detailed depictions of the surrounding environment at a high resolution.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

et al. 2020

View full text Add to dashboard Cite

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 paper 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 highperformance 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. INDEX TERMS Static probability, Scan-matching, Weighted NDT, LiDAR characteristic, Graph-based SLAM I. INTRODUCTION Light detection and ranging (LiDAR), which is an essential sensor in autonomous vehicles, has the following two characteristics. First, it can provide precise distance information on vehicles, buildings, and people in the form of points. Second, a LiDAR sensor can represent the surroundings of

show abstract

“…This log odds ratio can be accumulated as the sum of individual log odds ratios by the binary Bayes' filter in Equation (10).…”

Section: Final Static Probability Estimation By Integrating the Stmentioning

confidence: 99%

mentioning

confidence: 99%

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

et al. 2020

View full text Add to dashboard Cite

show abstract

2P2-C14 The generation of Environmental Map based on a High Resolutional NDT Grid Mapping

Cited by 1 publication

References 0 publications

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

Contact Info

Product

Resources

About