Visual Place Recognition is the task of matching a view of a place with a different view of the same place taken at a different time.
Source: Visual place recognition using landmark distribution descriptors
Image credit: Visual place recognition using landmark distribution descriptors
We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph.
This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task.
As we show, casting this to a key-value lookup problem can be achieved with k-means clustering, and results in a much simpler system than [1].
This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city.
3D RECONSTRUCTION AUTONOMOUS DRIVING STRUCTURE FROM MOTION VISUAL LOCALIZATION VISUAL PLACE RECOGNITION
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments.
As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving.
AUTONOMOUS DRIVING ROBOT NAVIGATION SALIENCY DETECTION SIMULTANEOUS LOCALIZATION AND MAPPING VISUAL PLACE RECOGNITION
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance.
SCENE UNDERSTANDING SEMANTIC SEGMENTATION VISUAL PLACE RECOGNITION
Point cloud based retrieval for place recognition is an emerging problem in vision field.
Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions.
AUTONOMOUS DRIVING IMAGE RETRIEVAL ROBOT NAVIGATION SELF-DRIVING CARS SIMULTANEOUS LOCALIZATION AND MAPPING VISUAL LOCALIZATION VISUAL NAVIGATION VISUAL PLACE RECOGNITION
In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features.