[go: up one dir, main page]

Abouee et al., 2024 - Google Patents

Weakly Supervised End2End Deep Visual Odometry

Abouee et al., 2024

View PDF
Document ID
12637839151261421167
Author
Abouee A
Ravi A
Hinneburg L
Dziwulski M
Ölsner F
Hess J
Milz S
Mäder P
Publication year
Publication venue
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

External Links

Snippet

Visual odometry is an ill-posed problem and utilized in many robotics applications especially automated driving for mapless navigation. Recent applications have shown that deep models outperform traditional approaches especially in localization accuracy and …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10016Video; Image sequence
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30248Vehicle exterior or interior
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10004Still image; Photographic image
    • G06T2207/10012Stereo images
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6288Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features

Similar Documents

Publication Publication Date Title
Alkendi et al. State of the art in vision-based localization techniques for autonomous navigation systems
Fu et al. PL-VINS: Real-time monocular visual-inertial SLAM with point and line features
US10371529B2 (en) Computational budget estimation for vision-aided inertial navigation systems
Moreau et al. Coordinet: uncertainty-aware pose regressor for reliable vehicle localization
Yin et al. Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields
US9709404B2 (en) Iterative Kalman Smoother for robust 3D localization for vision-aided inertial navigation
Krombach et al. Combining feature-based and direct methods for semi-dense real-time stereo visual odometry
Liu et al. Direct visual odometry for a fisheye-stereo camera
Zuo et al. Cross-modal semidense 6-dof tracking of an event camera in challenging conditions
Rückert et al. Snake-slam: Efficient global visual inertial slam using decoupled nonlinear optimization
Zhao et al. Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM
Françani et al. Dense prediction transformer for scale estimation in monocular visual odometry
Pirvu et al. Depth distillation: unsupervised metric depth estimation for UAVs by finding consensus between kinematics, optical flow and deep learning
Greene et al. Metrically-scaled monocular slam using learned scale factors
Liu et al. Real-time dense construction with deep multiview stereo using camera and imu sensors
Wu et al. Vings-mono: Visual-inertial gaussian splatting monocular slam in large scenes
Cheng et al. High precision and robust vehicle localization algorithm with visual-LiDAR-IMU fusion
Sun et al. GGC-SLAM: A VSLAM system based on predicted static probability of feature points in dynamic environments
Abouee et al. Weakly Supervised End2End Deep Visual Odometry
Mu et al. Visual navigation features selection algorithm based on instance segmentation in dynamic environment
Leng et al. Cross-modal LiDAR-visual-inertial localization in prebuilt LiDAR point cloud map through direct projection
Wang et al. Unsupervised scale network for monocular relative depth and visual odometry
Kuang et al. A real-time and robust monocular visual inertial slam system based on point and line features for mobile robots of smart cities toward 6g
Campos et al. Scale-aware direct monocular odometry
Cigla et al. Gaussian mixture models for temporal depth fusion