An Online Semantic Mapping System for Extending and Enhancing Visual SLAM
arXiv:2203.03944 · doi:10.1016/j.engappai.2022.104830
Abstract
We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Besides the semantic map enrichment the associated detections are further introduced as semantic constraints into a simultaneous localization and mapping (SLAM) system for pose correction purposes. This way, we are able generate additional meaningful information that allows to achieve higher-level tasks, while simultaneously leveraging the view-invariance of object detections to improve the accuracy and the robustness of the odometry estimation. We propose tracklets of locally associated object observations to handle ambiguous and false predictions and an uncertainty-based greedy association scheme for an accelerated processing time. Our system reaches real-time capabilities with an average iteration duration of 65~ms and is able to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a public dataset. Additionally, we implemented our approach as a modular ROS package that makes it straightforward for integration in arbitrary graph-based SLAM methods.
arXiv commentAccepted by Engineering Applications of Artificial Intelligence, Elsevier, 7 Mar 2022
References in corpus (5)
- YOLOv4: Optimal Speed and Accuracy of Object Detection
- DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
- RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
- Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
- Multiple Hypothesis Semantic Mapping for Robust Data Association