Nothing Special   »   [go: up one dir, main page]

Hong et al., 2024 - Google Patents

A stereo vision SLAM with moving vehicles tracking in outdoor environment

Hong et al., 2024

Document ID
5247585345628033333
Author
Hong C
Zhong M
Jia Z
You C
Wang Z
Publication year
Publication venue
Machine Vision and Applications

External Links

Snippet

The assumption of a static environment is a prerequisite for most of the traditional visual simultaneous localization and mapping (v-SLAM) algorithms, which limits their widespread application in a dynamic environment. Furthermore, in many applications such as …
Continue reading at link.springer.com (other versions)

Classifications

    • 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
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • G06K9/00791Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
    • 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
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • G06K9/20Image acquisition
    • G06K9/32Aligning or centering of the image pick-up or image-field
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content

Similar Documents

Publication Publication Date Title
US9933264B2 (en) System and method for achieving fast and reliable time-to-contact estimation using vision and range sensor data for autonomous navigation
Chen et al. Integrating stereo vision with a CNN tracker for a person-following robot
Lategahn et al. Vision-only localization
Servos et al. Multi-Channel Generalized-ICP: A robust framework for multi-channel scan registration
Volden et al. Vision-based positioning system for auto-docking of unmanned surface vehicles (USVs)
Mingachev et al. Comparison of ros-based monocular visual slam methods: Dso, ldso, orb-slam2 and dynaslam
EP3414641A1 (en) System and method for achieving fast and reliable time-to-contact estimation using vision and range sensor data for autonomous navigation
Wang et al. Non-iterative SLAM
Parra et al. Robust visual odometry for vehicle localization in urban environments
Poddar et al. Motion estimation made easy: Evolution and trends in visual odometry
Tang et al. Fmd stereo slam: Fusing mvg and direct formulation towards accurate and fast stereo slam
Hong et al. A stereo vision SLAM with moving vehicles tracking in outdoor environment
Guclu et al. A comparison of feature detectors and descriptors in rgb-d slam methods
Sahili et al. A Survey of Visual SLAM Methods
Tian et al. Object SLAM with robust quadric initialization and mapping for dynamic outdoors
Pershina et al. Methods of mobile robot visual navigation and environment mapping
Nwobodo et al. SLAM Methods for Augmented Reality Systems for Flight Simulators
Zhu et al. LVIF: a lightweight tightly coupled stereo-inertial SLAM with fisheye camera
Tian et al. DynaQuadric: Dynamic Quadric SLAM for Quadric Initialization, Mapping, and Tracking
Lomps et al. Evaluation of the robustness of visual slam methods in different environments
Chen et al. DynamicVINS: Visual-inertial localization and dynamic object tracking
Hou et al. Implicit map augmentation for relocalization
Aladem et al. Evaluation of a Stereo Visual Odometry Algorithm for Passenger Vehicle Navigation
Kandukuri et al. Physics-Based Rigid Body Object Tracking and Friction Filtering From RGB-D Videos
Rawashdeh et al. Scene Structure Classification as Preprocessing for Feature-Based Visual Odometry