Real-time object-oriented semantic map on VSLAM

WenYu Xu; ChunLin Ma; Liu Chun

doi:10.1117/12.2660016

3 February 2023 Real-time object-oriented semantic map on VSLAM

WenYu Xu, ChunLin Ma, Liu Chun

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111Q (2023) https://doi.org/10.1117/12.2660016
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

In view of the lack of real-time performance in most of the current object-level semantic map construction systems. Combined with high real-time instance segmentation network and visual synchronous location and map construction (VSLAM) algorithm, a object-oriented semantic map real-time construction system is proposed. firstly, the system uses the instance segmentation network to segment each color image, obtains the object and combines the feature and spatial information provided by VSLAM to form the instance description information, and then matches the instance through feature consistency and spatial consistency. At the same time, Bayes filter is used to calculate the existence state of instances and spatial constraints are used to filter the error detection of instances. finally, a real-time instance-level semantic map construction system based on VSLAM is realized, and the system is tested with TUM data sets. The results show that the system can build object-level semantic maps in real time and reduce the error detection rate of instance segmentation.

Citation Download Citation

WenYu Xu, ChunLin Ma, and Liu Chun "Real-time object-oriented semantic map on VSLAM", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111Q (3 February 2023); https://doi.org/10.1117/12.2660016

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