Accurate Visual Localization in Outdoor and Indoor Environments Exploiting 3d Image Spaces as Spatial Reference

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2020

Self Cite

Abstract. In this paper, we present a performance evaluation of our smartphone-based mobile mapping application based on an augmented reality (AR) framework in demanding outdoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluated our prototype system by comparing measured points from the tracked device with ground control points in an outdoor environment with four different campaigns. The campaigns consisted of open and closed-loop trajectories and different ground surfaces such as grass, concrete and gravel. Two campaigns passed a stairway in either direction. Our results show that the absolute 3D accuracy of device tracking with state-of-the-art AR framework on a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of a Mobile Mapping Application Using Smartphones and Augmented Reality Frameworks

Hasler

Blaser

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2020

Self Cite

“…Sattler et al (2018) distinguish the following visual localization categories: 3D structure-based, 2D image-based, sequence-based and learningbased localization. In our previous work, we introduced a method for visual localization and pose estimation of a single image to georeferenced RGB-D images (Nebiker et al, 2015;Rettenmund, Fehr, Cavegn, & Nebiker, 2018). This method works in indoor and outdoor environments and does not require ground control points.…”

Section: Related Workmentioning

confidence: 99%

Implementation and First Evaluation of an Indoor Mapping Application Using Smartphones and Ar Frameworks

Hasler

Blaser

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2019

Self Cite

Abstract. In this paper, we present the implementation of a smartphone-based indoor mobile mapping application based on an augmented reality (AR) framework and a subsequent performance evaluation in demanding indoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluate our prototype system by comparing measured points from the tracked device with ground control points in an indoor environment with two different campaigns. The first campaign consists of an open, one-way trajectory whereas the second campaign incorporates a loop closure. In the second campaign, the underlying AR framework successfully recognized the start location and correctly repositioned the device. Our results show that the absolute 3D accuracy of device tracking with a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.

“…In addition to database updating and asset management, there is a high demand for real-time device pose estimation for augmented reality applications, where 3D image spaces have a great potential for serving as reference data. First investigations of visual localization using 3D image spaces showed that large temporal differences and the associated changes in scene content and appearance are one of the main challenges in long-term visual localization (Rettenmund et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Long-Term Visual Localization in Large Scale Urban Environments Exploiting Street Level Imagery

Meyer

Rettenmund

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2020

Self Cite

Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dataset are nearly on par with the top ranked approaches, we focused our investigations on reproducibility and performance with own data. For this purpose, we created a dataset with street-level imagery. In order to have independent reference and query images, we used a road-based and a tram-based mapping campaign with a time difference of four years. The approximately 90% successfully oriented images of both datasets are a good indicator for the robustness of our approach. With about 50% success rate, every second image could be localized with a position accuracy better than 0.25 m and a rotation accuracy better than 2°.