RGB-D SLAM served as the design foundation for HDPV-SLAM, adding depth information to visual features. Abstract: This paper proposes a novel visual simultaneous localization and mapping (SLAM), called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), generating accurate and metrically scaled vehicle trajectories using a panoramic camera and a titled multi-beam LiDAR scanner.The experimental results reveal that the resilience property of PointNet is affected by our hybrid reverse signed perturbation strategy HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera We calculate the impact on model accuracy versus property factor and can test PointNet network’s robustness against a small collection of perturbing input states resulting from adversarial attacks like the suggested hybrid reverse signed attack. We have used extracted properties from the trained PointNet and changed certain factors for perturbation input. In this project, we describe a point cloud-based network verifier that successfully deals state of the art 3D classifier PointNet verifies the robustness by generating adversarial inputs. Because there will be always corner cases and adversarial input that can compromise the model’s effectiveness. It is difficult to conclude the robustness of a 3D vision model without performing the verification. Due to complex architecture, dimension of hyper-parameter, and 3D convolution, no verifiers can perform the basic layer-wise verification. Most of the existing verifiers work perfectly on 2D convolution. Despite their success, point cloud-based network models are vulnerable to multiple adversarial attacks, where the certain factor of changes in the validation set causes significant performance drop in well-trained networks. Abstract: 3D vision with real-time LiDAR-based point cloud data became a vital part of autonomous system research, especially perception and prediction modules use for object classification, segmentation, and detection.Subjects: Computer Vision and Pattern Recognition (cs.CV) Software Engineering (cs.SE).Authors: Arup Kumar Sarker, Farzana Yasmin Ahmad, Matthew B.There is no result Keyword: lidar PCV: A Point Cloud-Based Network Verifier Experimental results demonstrate that the proposed two modules significantly contribute to HDPV-SLAM’s performance, which outperforms the state-of-the-art (SOTA) SLAM systems. We assessed HDPV-SLAM’s performance using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. This hybrid depth association module intends to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the DL-based corrected depth during a phase of feature tracking. To overcome this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature triangulation and depth estimation. The second issue relates to the challenges in the depth association caused by a significant deficiency of horizontal overlapping coverage between the panoramic camera and the tilted LiDAR sensor. We address this issue by proposing a depth estimation module for iteratively densifying sparse LiDAR depth based on deep learning (DL). The first barrier is the sparseness of LiDAR depth, which makes it challenging to connect it with visual features extracted from the RGB image. It seeks to overcome the two problems that limit the performance of RGB-D SLAM systems. Subjects: Robotics (cs.RO) Computer Vision and Pattern Recognition (cs.CV). Authors: Mostafa Ahmadi, Amin Alizadeh Naeini, Zahra Arjmandi, Yujia Zhang, Mohammad Moein Sheikholeslami, Gunho Sohn.Key words: SLAM, odometry, livox, loam, lidar, loop detection, nerf, mapping, localization, transformer, autonomous driving Keyword: SLAM HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera
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