Dynamic dense CRF inference for video segmentation and semantic SLAM

Highlights：

• In this paper, we extend the traditional dense CRF inference algorithm to incremental sensor data modelling.

• The algorithm efficiently infers the maximum a posteriori probability (MAP) solution for a dynamically changing dense CRF model that is applied to incremental multi-class video segmentation and semantic SLAM.

• The computational cost is roughly proportional to the total change in the Gaussian pairwise edges of the dense CRF.

• In our system, with an increase in the number of frames of the sensor data, MAP calculations take approximately the same time to compute the overall three-dimensional dense CRF modelled for the entire video.

摘要

•In this paper, we extend the traditional dense CRF inference algorithm to incremental sensor data modelling.•The algorithm efficiently infers the maximum a posteriori probability (MAP) solution for a dynamically changing dense CRF model that is applied to incremental multi-class video segmentation and semantic SLAM.•The computational cost is roughly proportional to the total change in the Gaussian pairwise edges of the dense CRF.•In our system, with an increase in the number of frames of the sensor data, MAP calculations take approximately the same time to compute the overall three-dimensional dense CRF modelled for the entire video.

论文关键词：Incremental multi-class video segmentation,Semantic robotSimultaneous Localization and mMapping,Dynamic dense conditional random field

论文评审过程：Received 4 April 2021, Revised 27 August 2022, Accepted 4 September 2022, Available online 10 September 2022, Version of Record 15 September 2022.