Simulation of Dynamic Environments for SLAM
Abstract
Simulation engines are widely adopted in robotics. However, they lack either full simulation control, ROS integration, realistic physics, or photorealism. Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. However, when focusing on vision applications, there is usually a lack of information like sensor measurements or time continuity. On the other hand, simulations for most robotics tasks are performed in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we introduced in our previous work a fully customizable framework for generating realistic animated dynamic environments (GRADE) [1]. We use GRADE to generate an indoor dynamic environment dataset and then compare multiple SLAM algorithms on different sequences. By doing that, we show how current research over-relies on known benchmarks, failing to generalize. Our tests with refined YOLO and Mask R-CNN models provide further evidence that additional research in dynamic SLAM is necessary. The code, results, and generated data are provided as open-source at https://eliabntt.github.io/grade-rrSimulation of Dynamic Environments for SLAM
Cite
@article{arxiv.2305.04286,
title = {Simulation of Dynamic Environments for SLAM},
author = {Elia Bonetto and Chenghao Xu and Aamir Ahmad},
journal= {arXiv preprint arXiv:2305.04286},
year = {2023}
}
Comments
CITE AS: @inproceedings{ bonetto2023dynamicSLAM, title={{S}imulation of {D}ynamic {E}nvironments for {SLAM}}, author={Elia Bonetto and Chenghao Xu and Aamir Ahmad}, booktitle={ICRA2023 Workshop on Active Methods in Autonomous Navigation}, year={2023}, url={https://robotics.pme.duth.gr/workshop_active2/wp-content/uploads/2023/05/01.-Simulation-of-Dynamic-Environments-for-SLAM.pdf} }. arXiv admin note: substantial text overlap with arXiv:2303.04466