Superpixel-based appearance change prediction for long-term navigation across seasons

Neubert, Peer; Sünderhauf, Niko; Protzel, Peter

doi:10.1016/j.robot.2014.08.005

Cited by 71 publications

(52 citation statements)

References 22 publications

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“…The Synthesized Nordland dataset was recorded from a camera mounted on a train. The first traverse was recorded in spring and the second in winter (see [21] for a more detailed introduction). The Berlin A100, Berlin Halenseestrasse and the Berlin Kudamm datasets were all downloaded from a crowdsourced photo-mapping platform called Mapillary 2 .…”

Section: A Datasetsmentioning

confidence: 99%

Only look once, mining distinctive landmarks from ConvNet for visual place recognition

Chen

Maffra

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

147

209

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-Recently, image representations derived from Convolutional Neural Networks (CNNs) have been demonstrated to achieve impressive performance on a wide variety of tasks, including place recognition. In this paper, we take a step deeper into the internal structure of CNNs and propose novel CNN-based image features for place recognition by identifying salient regions and creating their regional representations directly from the convolutional layer activations. A range of experiments is conducted on challenging datasets with varied conditions and viewpoints. These reveal superior precision-recall characteristics and robustness against both viewpoint and appearance variations for the proposed approach over the state of the art. By analyzing the feature encoding process of our approach, we provide insights into what makes an image presentation robust against external variations.

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Section: A Datasetsmentioning

confidence: 99%

Only look once, mining distinctive landmarks from ConvNet for visual place recognition

Chen

Maffra

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

147

209

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“…Main properties of all datasets are listed in Table 1. Sample images are shown in Figure 1. (a) For the UACampus [35] [37] dataset, spring and winter subsets were used, because they exhibit the greatest variation in appearance caused by the seasonal changes. In our experiments, we used 1000 images uniformly sampled from each of the two subsets.…”

Section: Testing Datasetsmentioning

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

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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.

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“…Recently, some authors proposed to exploit these conflicting measurements in order to obtain information about the world dynamics and proposed representations that model the environment dynamics explicitly. These dynamic representations have shown their potential by improving mobile robot localization in changing environments [3], [4], [5], [6].…”

Section: Tkrajnik@lincolnacukmentioning

confidence: 99%

“…Kucner's method [20] learns conditional probabilities of neighbouring cells of an occupancy grid to model typical motion patterns in dynamic environments. Another team proposed a method that can learn appearance changes based on an across-seasons dataset and use the learned model to predict the environment appearance for a given time [6].…”

Section: B Dynamic Environment Representationsmentioning

confidence: 99%

Life-long spatio-temporal exploration of dynamic environments

Krajník

Santos

Duckett

2015

2015 European Conference on Mobile Robots (ECMR)

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Abstract-We propose a new idea for life-long mobile robot spatio-temporal exploration of dynamic environments. Our method assumes that the world is subject to constant change, which adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process. To create and maintain a spatio-temporal model of a dynamic environment, the robot has to determine not only where, but also when to perform observations. We address the problem by application of information-theoretic exploration to world representations that model the environment states' uncertainties as probabilistic functions of time.We compare the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months and show that combination of dynamic environment representations with information-gain exploration principles allow to create and maintain up-to-date models of constantly changing environments.

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Superpixel-based appearance change prediction for long-term navigation across seasons

Cited by 71 publications

References 22 publications

Only look once, mining distinctive landmarks from ConvNet for visual place recognition

Only look once, mining distinctive landmarks from ConvNet for visual place recognition

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

Life-long spatio-temporal exploration of dynamic environments

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