Robust Visual Localization Across Seasons

Naseer, Tayyab; Burgard, Wolfram; Stachniss, Cyrill

doi:10.1109/tro.2017.2788045

Cited by 169 publications

(175 citation statements)

References 44 publications

Supporting

Mentioning

174

Contrasting

Order By: Relevance

“…This approach, SMART PF, shows better results than SeqSLAM. Another interesting approach is shown in [141] where the algorithm is tested to find loop closures across different seasons. To do so, the problem is also solved by using image sequences.…”

Section: A Relocalization and Loop Closurementioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

show abstract

Section: A Relocalization and Loop Closurementioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

show abstract

“…In addition, the advantages of ConvNet features in environments with various changes have been further confirmed by another evaluation study [27]. Since then, ConvNet features have been widely applied to improve some existing visual localization methods such as SeqSLAM [7] and a seasonrobust method using network flows [11], where hand-crafted features were replaced by ConvNet features [12,32].…”

Section: Related Workmentioning

confidence: 93%

“…In particular, recent interest in autonomous vehicles has created a strong need for visual localization techniques that can efficiently operate in challenging environments. Although current stateof-the-art approaches have made great strides [2][3][4][5][6][7][8][9][10][11][12], visual localization for long-term navigation of autonomous vehicles still remains an unsolved problem when image appearance experiences significant changes caused by time of the day, season, weather, camera pose, etc. [1].…”

Section: Introductionmentioning

confidence: 99%

Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

Hou

Zhang

Zhou

et al. 2017

Mobile Information Systems

View full text Add to dashboard Cite

Efficient and robust visual localization is important for autonomous vehicles. By achieving impressive localization accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention of people in several research communities including autonomous vehicles. Such an approach relies heavily on the outstanding discrimination power of ConvNet features to match detected landmarks between images. However, a major challenge of this approach is how to extract discriminative ConvNet features efficiently. To address this challenging, inspired by the high efficiency of the region of interest (RoI) pooling layer, we propose a Multiple RoI (MRoI) pooling technique, an enhancement of RoI, and a simple yet efficient ConvNet feature extraction method. Our idea is to leverage MRoI pooling to exploit multilevel and multiresolution information from multiple convolutional layers and then fuse them to improve the discrimination capacity of the final ConvNet features. The main advantages of our method are (a) high computational efficiency for real-time applications; (b) GPU memory efficiency for mobile applications; and (c) use of pretrained model without fine-tuning or retraining for easy implementation. Experimental results on four datasets have demonstrated not only the above advantages but also the high discriminating power of the extracted ConvNet features with state-of-the-art localization accuracy.

show abstract

“…Deep features based on convolutional neural networks (CNNs) were adopted to match image sequences [31]. Global features can encode whole image information and no dictionary-based quantization is required, which showed promising performance for long-term place recognition [2,26,27,30,33].…”

Section: A Visual Features For Scene Representationmentioning

confidence: 99%

“…Other sequence-based matching methods were also proposed. For example, this problem is formulated in [30] as a minimum cost flow task in a data association graph to exploit sequence information. Hidden Markov Models (HMMs) [15] and Conditional Random Fields (CRFs) [4] were also applied to align a pair of template and query sequences.…”

Section: B Image Matching For Place Recognitionmentioning

confidence: 99%

Robust Multimodal Sequence-Based Loop Closure Detection via Structured Sparsity

Zhang¹,

Han²,

Wang³

Robotics: Science and Systems XII

View full text Add to dashboard Cite

Abstract-Loop closure detection is an essential component for simultaneously localization and mapping in a variety of robotics applications. One of the most challenging problems is to perform long-term place recognition with strong perceptual aliasing and appearance variations due to changes of illumination, vegetation, weather, etc. To address this challenge, we propose a novel Robust Multimodal Sequence-based (ROMS) method for long-term loop closure detection, by formulating image sequence matching as an optimization problem regularized by structured sparsity-inducing norms. Our method is able to model the sparsity nature of place recognition, i.e., the current location should match only a small subset of previously visited places, as well as to model underlying structures of image sequences and incorporate multiple feature modalities to construct a discriminative scene representation. In addition, a new optimization algorithm is developed to efficiently solve the formulated problem, which has a theoretical guarantee to converge to the global optimal solution. To evaluate the ROMS algorithm, extensive experiments are performed using large-scale benchmark datasets, including St Lucia, CMU-VL, and Nordland datasets. Experimental results have validated that our algorithm outperforms previous loop closure detection methods, and obtains the state-of-the-art performance on long-term place recognition.

show abstract

Robust Visual Localization Across Seasons

Cited by 169 publications

References 44 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

Robust Multimodal Sequence-Based Loop Closure Detection via Structured Sparsity

Contact Info

Product

Resources

About