Appearance‐based landmark selection for visual localization

Bürki, Mathias; Cadena, César; Gilitschenski, Igor; Siegwart, Roland; Nieto, Juan

doi:10.1002/rob.21870

Cited by 19 publications

(22 citation statements)

References 36 publications

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“…Localization: Over the last two decades, the robotics community extensively considered the problem of localization and mapping [15] involving a diverse set of sensors, most prominently cameras [16] and lidars [2]- [4]. Particularly for autonomous driving, a considerable amount of work focused on dealing with challenging and changing appearance conditions [9] such as weather [17] or occlusions [7], [18]. To improve robustness, radar has also been considered as a localization modality for autonomous driving [19]- [22].…”

Section: Related Workmentioning

confidence: 99%

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GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

Ort¹,

Gilitschenski²,

Rus³

2021

Robotics: Science and Systems XVII

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Mapping and localization using surface features is prone to failure due to environment changes such as inclement weather. Recently, Localizing Ground Penetrating Radar (LGPR) has been proposed as an alternative means of localizing using underground features that are stable over time and less affected by surface conditions. However, due to the lack of commercially available LGPR sensors, the wider research community has been largely unable to replicate this work or build new and innovative solutions. We present GROUNDED an open dataset of LGPR scans collected in a variety of environments and weather conditions. By labeling this data with ground truth localization from an RTK-GPS / Inertial Navigation System, and carefully calibrating and time synchronizing the radar scans with ground truth positions, camera imagery, and Lidar data, we enable researchers to build novel localization solutions that are resilient to changing surface conditions. We include 108 individual runs totalling 450 km of driving with LGPR, GPS, Odometry, Camera, and Lidar measurements. We also present two new evaluation benchmarks for 1) Localizing in Weather and 2) Multi-lane Mapping, to enable comparisons of future work supported by the dataset. The dataset can be accessed at http://lgprdata.com.

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Section: Related Workmentioning

confidence: 99%

“…Some approaches aim to filter out dynamic objects during mapping [7]. Others seek to identify and map only stable features or landmarks in the environment [8], [9]. Robustly dealing with inclement weather such as snow is particularly challenging as snowfall can dramatically alter the surface appearance.…”

Section: Introductionmentioning

confidence: 99%

GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

Ort¹,

Gilitschenski²,

Rus³

2021

Robotics: Science and Systems XVII

Self Cite

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show abstract

“…The patches with excessively small or large sizes, and those with evident variation in height and width are regarded as the unqualified patches, because they would suffer severe deformation in the resizing operation, and it's essential to delete those unqualified patches from the current patches set. To achieve that, we define the constraints of patch size, as listed in Equations (1) and (2), where W and H denote the width and height of the detected patches, respectively. All the detected patches are traversed, and the sizes of them are strictly checked.…”

Section: Patch Detectionmentioning

confidence: 99%

“…All the detected patches are traversed, and the sizes of them are strictly checked. With respect to each patch, if the width and height cannot follow the constraints in Equations (1) and (2), the patch is deleted from the current patches set. Consequently, the remaining patches are all qualified, their sizes are moderate, and their shapes are comparatively square.…”

Section: Patch Detectionmentioning

confidence: 99%

PL‐VSCN: Patch‐level vision similarity compares network for image matching

Xiong

Qin

et al. 2021

IET Computer Vision

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Image matching plays an important role in various computer vision tasks, such as image retrieval and loop closure detection in Simultaneous Localization and Mapping. The authors propose a discriminative patch-based image matching method that converts the problem of whole image matching to that of local patch matching. To construct the patch representation, the Patch-Level Vision Similarity Compare Network (PL-VSCN) is proposed to produce the patch feature. In the image matching process, local patches that potentially contain objects within images are initially detected, and the discriminative feature of each patch is extracted based on the pre-trained PL-VSCN. Then, the similarities between the patch pairs are calculated to construct the similarity matrix, and the corresponding patch pairs are detected based on the mutual matching mechanism on the similarity matrix. Experimental results indicate that the proposed PL-VSCN can generate the discriminative patch feature, which can accurately match the patch pairs with the corresponding content and distinguish those with non-corresponding content. In addition, the comparison experiments demonstrate that the proposed image matching method outperforms existing approaches on most datasets and effectively completes the image matching task. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Particularly for visual SLAM [1], [2], an extensive body of research explicitly focuses on addressing these types of problems. The breadth of approaches involves inpainting and removal of dynamic objects [12], [13], selecting particularly persistent landmarks for map storage [14], and selecting appearance specific landmarks [15] or map segments [16]. We avoid the problems addressed in this line of work altogether by using sub-surface features that do not suffer from frequent appearance changes and occlusions.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

Ort

Gilitschenski

Rus

2020

IEEE Robot. Autom. Lett.

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Most autonomous driving solutions require some method of localization within their environment. Typically, onboard sensors are used to localize the vehicle precisely in a previously recorded map. However, these solutions are sensitive to ambient lighting conditions such as darkness and inclement weather. Additionally, the maps can become outdated in a rapidly changing environment and require continuous updating. While LiDAR systems don't require visible light, they are sensitive to weather such as fog or snow, which can interfere with localization. In this letter, we utilize a Ground Penetrating Radar (GPR) to obtain precise vehicle localization. By mapping and localizing using features beneath the ground, we obtain features that are both stable over time, and maintain their appearance during changing ambient weather and lighting conditions. We incorporate this solution into a full-scale autonomous vehicle and evaluate the performance on over 17 km of testing data in a variety of challenging weather conditions. We find that this novel sensing modality is capable of providing precise localization for autonomous navigation without using cameras or LiDAR sensors. Index Terms-Autonomous vehicle navigation, field robots, wheeled robots, intelligent transportation systems. I. INTRODUCTIONR OBUST localization in diverse conditions is a key challenge to enable the widespread deployment of autonomous vehicles. Since relying purely on a global navigation satellite system (GNSS), such as the Global Positioning System (GPS) does not provide sufficient precision, research and industry efforts have focused primarily on utilizing cameras and laser scanners for the navigation task. These systems typically use Simultaneous Localization and Mapping (SLAM) algorithms [1] for creating and maintaining maps of the environment that allow for highly precise localization and navigation.Using vision and laser sensors as localization sources comes with its own set of challenges. For instance, localization from purely visual maps needs to account for the fact that, even in the absence of occlusions, the appearance of the environment strongly varies depending on weather, season, time of day, and a potentially changing environment [2]. While laser scanners

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Appearance‐based landmark selection for visual localization

Abstract: Visual localization in outdoor environments is subject to varying appearance conditions rendering it difficult to match current camera images against a previously recorded map. Although it is possible to extend the respective maps to allow precise

Cited by 19 publications

References 36 publications

GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

PL‐VSCN: Patch‐level vision similarity compares network for image matching

Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar

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