Multimodal visual position recognition based on dynamic invariant perception

Original link: Open source soon! Multimodal visual position recognition based on dynamic invariant perception
Title: multi modal visual place recognition in dynamics invariant perception space

**From: * * School of automation, Southeast University

**Author: * * Lin Wu, Teng Wang and Changyin Sun

Link: https://doi.org/10.1109/LSP.2021.3123907

Code address (to be open source): https://github.com/fiftywu/Multimodal-VPR

Abstract

Visual position recognition is one of the essential and challenging problems in the field of robot. In this newsletter, we first explore the use of semantic and visual multimodal fusion in dynamic invariant space to improve location recognition in dynamic environment. Firstly, we design a novel deep learning architecture to generate static semantic segmentation and recover the static image directly from the corresponding dynamic image. Then, we use spatial pyramid matching model (SPM) to encode static semantic segmentation into feature vectors, while for static images, we use the popular word bag model (BoW) to encode. Based on the above multimodal features, we measure the similarity between the query image and the target landmark through the joint similarity of semantic and visual coding. A large number of experiments show the effectiveness and robustness of the proposed method in dynamic environment.

Visual position recognition

Visual position recognition (VPR), as a key component of SLAM system, is a task that can help robot determine whether it is located in the place it has visited previously. The current work usually regards it as an image retrieval task to match the current observation with a set of reference landmarks, and designs various feature descriptors to measure the similarity of landmarks. These methods usually assume that the system runs in a static environment. However, the real world is complex and dynamic. The existence of dynamic objects makes the appearance of the scene inconsistent at different times, which increases the error of feature matching.

Dynamic invariant perception

Dynamic invariant perception refers to the elimination and transformation of dynamic content (such as pedestrians and vehicles) into corresponding static content in a dynamic scene. Typical work includes empty cities: a dynamic object invariant space for visual Slam (IEEE Transactions on Robotics,2020). On this basis, we have made some improvements and proposed a coarse to fine approach for dynamic to static image translation (Pattern Recognition, 2021). In the IEEE-SPL express, we design a novel deep neural network architecture to directly infer static semantics (i.e. static semantic segmentation graph) and static images from the input static scene images. In particular, we also use static semantics as a priori to improve the quality of static image generation. The static semantic segmentation results and static image conversion effects are shown in Figure 2 and figure 3 (the experimental data set is created by driverless simulator CARLA).

Visual position recognition experiment

In order to compare with the VPR recall rate of the current mainstream image conversion methods, we use Pix2Pix, MGAN, SRMGAN and SSGGNet to restore the static image, and then extract the BoW feature from it to measure the image similarity. The recall accuracy of different models is given in the table. In contrast, our method uses BoW and SPM coding at the same time, which performs best, and greatly improves the recall rate of the second SSGGNet BoW, which fully reflects the importance of semantic features based on SPM. In addition, SSGGNet BoW is better than Pix2Pix BoW, MGAN BoW and SRMGAN BoW, which further verifies the effectiveness of using static semantics to guide static image generation.

Related papers

T. Wang, L. Wu and C. Sun, "A coarse-to-fine approach for dynamic-to-static image translation," in Pattern Recognition, 2022, doi: 10.1016/j.patcog.2021.108373.

L. Wu, T. Wang and C. Sun, "Multi-Modal Visual Place Recognition in Dynamics-Invariant Perception Space," in IEEE Signal Processing Letters, 2021, doi: 10.1109/LSP.2021.3123907.

B. Bescos, C. Cadena and J. Neira, "Empty Cities: A Dynamic-Object-Invariant Space for Visual SLAM," in IEEE Transactions on Robotics, 2021, doi: 10.1109/TRO.2020.3031267.

P. Isola, J. Zhu, T. Zhou and A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks", CVPR, 2017, https://arxiv.org/pdf/1611.07004.pdf.

  

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Posted on Wed, 01 Dec 2021 00:04:23 -0500 by allex01