SLAM using ICP and graph optimization considering physical properties of environment

Suzuki, Ryuki; Kataoka, Ryosuke; Ji, Yonghoon; Umeda, Kazunori; Fujii, Hiromitsu; Kono, Hitoshi

doi:10.1109/rem49740.2020.9313074

Cited by 3 publications

(2 citation statements)

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“…This is achieved by map-matching of lidar scans [33]. Well-known readily available algorithms such as Normalized Distribution Transform (NDT) and Iterative Closest Point (ICP) can be used in real time for this map-matching-based localization [14,[34][35][36]. Unfortunately, map-matching-based localization cannot be re-created in a proving ground, as the surrounding buildings, trees, infrastructure, etc.…”

Section: Related Workmentioning

confidence: 99%

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

Xiao

Chen

Gelbal

et al. 2023

Sensors

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The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.

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Section: Related Workmentioning

confidence: 99%

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

Xiao

Chen

Gelbal

et al. 2023

Sensors

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“…Figure 1 shows the complete framework of the SLAM algorithm in this paper, firstly, the input is subjected to feature extraction based on the improved balanced quadtree method to obtain uniformly distributed feature points, and the GMS method is used to improve the matching efficiency between neighboring frames. When target detection is performed on the input, the a priori information of the moving object is passed into the dynamic processing thread, which is filtered to obtain the feature points of the static object, and the Iterative Closest Point (ICP) [17] algorithm is used to solve for the camera position and complete the camera localization.…”

Section: System Structure a System Frameworkmentioning

confidence: 99%

A Dense Visual SLAM Method in Dynamic Scenes

Liu,

Zhang,

Wang

et al. 2023

IEEE Access

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The visual SLAM algorithm has been developed to be more mature. It can achieve good performance in most static scenes, but when it is applied to dynamic scenes, it will be interfered with by a large number of dynamic points in the scene, which affects the system accuracy and even leads to tracking failures, and the sparse maps built by traditional SLAM algorithms can not satisfy advanced needs such as navigation. Aiming at the above problems, this paper proposes a dense visual SLAM method in dynamic scenes. In this paper, based on the ORB-SLAM2 algorithm, we use the improved balanced quadtree method to obtain uniformly distributed feature points, use the motion grid statistics method to improve the accuracy of feature matching between neighboring frames, add a new target detection network to obtain the a priori information of the dynamic objects, and then construct a dense map containing only static information in the dense map building thread. Finally, to verify the effectiveness of the algorithms in this paper, the TUM dataset is used to compare with the algorithms of ORB-SLAM2, ORB-SLAM3, DS-SLAM, and RDS-SLAM. The results show that this paper's SLAM algorithm improves 97.54%, 97.35%, 28.74%, and 17.37% on the root mean square error of the absolute trajectory error compared to the four algorithms in the measured highdynamic sequences, respectively.

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