Features for Ground Texture Based Localization -- A Survey
Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveySimilar Papers 제목 키워드 기반
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce a…
Vocal Bursts Intensity PredictionModel-Based Parameter Optimization for Ground Texture Based Localization Methods
A promising approach to accurate positioning of robots is ground texture based localization. It is based on the observation that visual features of ground images enable fingerprint-like place recognition. We tackle the i…
Ground Texture Based Localization Using Compact Binary Descriptors
Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local loca…
Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization
Ground texture localization using a downward-facing camera offers a low-cost, high-precision localization solution that is robust to dynamic environments and requires no environmental modification. We present a significa…
Image RetrievalLoop Closure DetectionMonocular Localization with Semantics Map for Autonomous Vehicles
Accurate and robust localization remains a significant challenge for autonomous vehicles. The cost of sensors and limitations in local computational efficiency make it difficult to scale to large commercial applications.…
Autonomous DrivingAutonomous VehiclesComputational EfficiencyVisual Localization