Robot Localization Using a Learned Keypoint Detector and Descriptor with a Floor Camera and a Feature Rich Industrial Floor
The localization of moving robots depends on the availability of good features from the environment. Sensor systems like Lidar are popular, but unique features can also be extracted from images of the ground. This work presents the Keypoint Localization Framework (KOALA), which utilizes deep neural networks that extract sufficient features from an industrial floor for accurate localization without having readable markers. For this purpose, we use a floor covering that can be produced as cheaply as common industrial floors. Although we do not use any filtering, prior, or temporal information, we can estimate our position in 75.7 % of all images with a mean position error of 2 cm and a rotation error of 2.4 %. Thus, the robot kidnapping problem can be solved with high precision in every frame, even while the robot is moving. Furthermore, we show that our framework with our detector and descriptor combination is able to outperform comparable approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
PositionSimilar Papers 제목 키워드 기반
R2D2: Reliable and Repeatable Detector and Descriptor
Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical approaches are based on a detect-then-describe paradigm where separate handcrafted methods are …
Atari GamesCamera LocalizationImage MatchingInterest Point Detection+2KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping
Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robustness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either indepe…
Point Cloud RegistrationSelf-Supervised LearningR2D2: Repeatable and Reliable Detector and Descriptor
Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted…
Interest Point DetectionKeypoint DetectionMetric LearningAnalysis of feature detector and descriptor combinations with a localization experiment for various performance metrics
The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of …
MatChA: Cross-Algorithm Matching with Feature Augmentation
State-of-the-art methods fail to solve visual localization in scenarios where different devices use different sparse feature extraction algorithms to obtain keypoints and their corresponding descriptors. Translating feat…
Visual Localization