Multi-modal Semantic Place Classification

Pronobis, Andrzej; Mozos, Óscar Martı́nez; Caputo, Barbara; Jensfelt, Patric

doi:10.1177/0278364909356483

Cited by 139 publications

(112 citation statements)

References 40 publications

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“…[1] achieved high accuracy on recognizing places, while for categorizing, [1]'s accuracy was not satisfactory. Pronobis et al [3,4,10,11] extensively studied place recognition and categorization. [10,11] focused on addressing place recognition under various weather and illumination conditions.…”

Section: Relates Workmentioning

confidence: 99%

“…[10,11] focused on addressing place recognition under various weather and illumination conditions. Later works [3,4] tried to solve the place categorization problem in a hierarchical and multi-modal way. Pronobis et al also published several datasets in order to set up standard benchmark [4,12].…”

Section: Relates Workmentioning

confidence: 99%

“…Later works [3,4] tried to solve the place categorization problem in a hierarchical and multi-modal way. Pronobis et al also published several datasets in order to set up standard benchmark [4,12]. In [13], Ullah et al used a SVM-based method to address place recognition and categorization on the dataset COsy Localization Database (COLD) [12].…”

Section: Relates Workmentioning

confidence: 99%

“…The emphasis on "visual" makes VPC different from place categorization problem addressed by the robotics community [3,4], which usually make use of other sensory data such as laser. The emphasis on "autonomous" and "image sequences" makes it different from the scene categorization/recognition problem in computer vision that focuses on recognizing the semantic category of a single image captured by a person.…”

Section: Introductionmentioning

confidence: 99%

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Object Templates for Visual Place Categorization

Yang

2013

Computer Vision – ACCV 2012

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Abstract. The Visual Place Categorization (VPC) problem refers to the categorization of the semantic category of a place using only visual information collected from an autonomous robot. Previous works on this problem only made use of the global configurations observation, such as the Bag-of-Words model and spatial pyramid matching. In this paper, we present a novel system solving the problem utilizing both global configurations observation and local objects information. To be specific, we propose a local objects classifier that can automatically and effectively select key local objects of a semantic category from randomly sampled patches by the structural similarity support vector machine; and further classify the test frames with the Local Naive Bayes Nearest Neighbors algorithm. We also improve the global configurations observation with histogram intersection codebook and a noisy codewords removal mechanism. The temporal smoothness of the classification results is ensured by employing a Bayesian filtering framework. Empirically, our system outperforms state-of-the-art methods on two large scale and difficult datasets, demonstrating the superiority of the system.

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Section: Relates Workmentioning

confidence: 99%

Section: Relates Workmentioning

confidence: 99%

Section: Relates Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object Templates for Visual Place Categorization

Yang

2013

Computer Vision – ACCV 2012

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“…Many approaches that generalize well, learn classifiers, such as SVMs, for each place from labeled data [25], [27]. A very recent system by Pronobis et al [24] focusses on merging cues from different sensing modalities, the output for each of which is obtained from individually trained SVMs. These, and similar classifier-based approaches cannot detect or learn previously unknown places and place categories, in contrast to PLISS.…”

Section: Related Workmentioning

confidence: 99%

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

Ranganathan¹

2010

Robotics: Science and Systems VI

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Abstract-We present PLISS (Place Labeling through Image Sequence Segmentation), a novel technique for place recognition and categorization from visual cues. PLISS operates on video or image streams and works by segmenting it into pieces corresponding to distinct places in the environment. An online Bayesian change-point detection framework that detects changes to model parameters is used to segment the image stream. Unlike current place recognition methods, in addition to using previously learned place models for labeling, PLISS can also detect and learn a previously unknown place or place category in an online manner. Moreover, since both the inferred boundaries of places (change-points) and the place labels are fully probabilistic, they can indicate when the inference is uncertain. New places and categories are detected using a systematic statistical hypothesis testing framework. We present extensive experiments on a large and difficult image dataset. We validate our claims by comparing results obtained using different types of features and by comparing results from PLISS against the state of the art.

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Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

2014

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While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle-friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules-place recognition, landmark recognition, and road lane detectionsupplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid-occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real-time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route. C 2014 Wiley Periodicals, Inc.

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Multi-modal Semantic Place Classification

Abstract: Abstract

Cited by 139 publications

References 40 publications

Object Templates for Visual Place Categorization

Object Templates for Visual Place Categorization

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

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