Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference

TL;DR

This paper introduces a dynamic semantic mapping framework that integrates 3D scene flow and Bayesian inference to produce continuous, high-resolution 3D semantic occupancy maps, effectively handling moving objects in complex environments.

Contribution

The paper presents a novel Bayesian inference model that incorporates 3D scene flow measurements for dynamic semantic mapping, outperforming static models and previous methods.

Findings

Improved accuracy of dynamic semantic maps over static models.

Consistent performance gains using deep learning-based measurements.

Effective handling of dynamic objects in complex scenes.

Abstract

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.