The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor.In this paper we propose a framework, implemented in the PCL library, which provides a set of valuable tools to easily develop and evaluate semantic localization systems. The implementation includes the generation of 3D global descriptors following a Bag-of-Words approach. This allows the generation of fixed-dimensionality descriptors from any type of keypoint detector and feature extractor combinations. The framework has been designed, structured and implemented to be easily extended with different keypoint detectors, feature extractors as well as classification models.The proposed framework has also been used to evaluate the performance of a set of already implemented descriptors, when used as input for a specific semantic localization system. The obtained results are discussed paying special attention to the internal parameters of the BoW descriptor generation process. Moreover, we also review the combination of some keypoint detectors with different 3D descriptor generation techniques. Presentation of a BoW implementation in the Point Cloud Library.Proposal of a general framework for semantic localization systems.The framework allows for integrations of future 3D features and keypoints.The Harris3D detector outperforms uniform sampling with fewer detected keypoints.BoW descriptors obtain better results than the ESF global feature.
For a complete review of the state-of-art in semantic localization we refer the reader to @cite_2 where a survey on this subject has been recently published. However, let's review the most related previous works from the last recent years.
@cite_2
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Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks, its applicability to state estimation tasks has been limited due to the direct formulation that renders it incapable of encoding scene-specific constrains. In this work, we propose the VLocNet++ architecture that employs a multitask learning approach to exploit the inter-task relationship between learning semantics, regressing 6-DoF global pose and odometry, for the mutual benefit of each of these tasks. Our network overcomes the aforementioned limitation by simultaneously embedding geometric and semantic knowledge of the world into the pose regression network. We propose a novel adaptive weighted fusion layer to aggregate motion-specific temporal information and to fuse semantic features into the localization stream based on region activations. Furthermore, we propose a self-supervised warping technique that uses the relative motion to warp intermediate network representations in the segmentation stream for learning consistent semantics. Finally, we introduce a first-of-a-kind urban outdoor localization dataset with pixel-level semantic labels and multiple loops for training deep networks. Extensive experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc dataset demonstrate that our approach exceeds the state-of-the-art outperforming local feature-based methods while simultaneously performing multiple tasks and exhibiting substantial robustness in challenging scenarios. The problem of localization and geo-location estimation of an image has a long-standing history both in robotics and computer vision. With the advent of availability of large amounts of geo-referenced image data, several image retrieval approaches have been deployed to tackle this problem. In this work, we will show how the capability of semantic labeling of both query views and the reference dataset by means of semantic segmentation can aid (1) the problem of retrieval of views similar and possibly overlapping with the query and (2) guide the recognition and discovery of commonly occurring scene layouts in the reference dataset. We will demonstrate the effectiveness of these semantic representations on examples of localization, semantic concept discovery, and intersection recognition in the images of urban scenes. This paper presents the second round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2013. The task was designed to promote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Participants were presented with datasets for different language pairs, where multi-directional entailment relations (“forward”, “backward”, “bidirectional”, “no entailment”) had to be identified. We report on the training and test data used for evaluation, the process of their creation, the participating systems (six teams, 61 runs), the approaches adopted and the results achieved. Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional feature-based methods often struggle in these conditions due to the significant number of erroneous matches between the image and the 3D model. In this paper, we present a method for scoring the individual correspondences by exploiting semantic information about the query image and the scene. In this way, erroneous correspondences tend to get a low semantic consistency score, whereas correct correspondences tend to get a high score. By incorporating this information in a standard localization pipeline, we show that the localization performance can be significantly improved compared to the state-of-the-art, as evaluated on two challenging long-term localization benchmarks.