Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

Jo, Kichun; Cho, Sung‐Jin; Kim, Chansoo; Resende, Paulo; Bradai, Benazouz; Nashashibi, Fawzi; Sunwoo, Myoungho

doi:10.3390/s18124119

Cited by 12 publications

(10 citation statements)

References 22 publications

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“…9. Using the p best , the best matched pose x t,best , composed of latitude, longitude, height, and heading angle, can be derived as (14).…”

Section: B Optimization Approachmentioning

confidence: 99%

“…Whereas the geometric shapes are represented by numerous points in Cartesian coordinates. Although Cartesian coordinates modeling the plane region can be applied in narrow regions for the purpose of demonstrations, the coordinates necessarily occur coordinate conversion errors to represent the Earth which is an orb [13], [14] as shown in Figure 1-(b). The coordinate difference between geodetic and Cartesian coordinates causes localization errors, and the errors are bigger in larger regions.…”

Section: Introductionmentioning

confidence: 99%

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A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

et al. 2021

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“…9. Using the p best , the best matched pose x t,best , composed of latitude, longitude, height, and heading angle, can be derived as (14).…”

Section: B Optimization Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

et al. 2021

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“…Graph SLAM optimizes the vehicle poses using point cloud registration as an optimization constraint [ 14 ]. In [ 15 ], Graph SLAM was adopted for the grid mapping based on an evidential theory. Alternatively, Zhang et al proposed a low-drift and online LiDAR-only mapping method [ 16 , 17 ], and [ 18 ] introduced LeGO-LOAM using geometric feature-based LiDAR odometry estimation for a low-power embedded system.…”

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

Sensors

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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.

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“…An occupancy grid map is a static map of the surrounding environment created by accumulating measured values [15]. The surrounding environment must be divided into a 2D or 3D space to make the map.…”

Section: A Classification Of Dynamic Objectsmentioning

confidence: 99%

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

et al. 2020

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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

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Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

Cited by 12 publications

References 22 publications

A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

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

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

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