OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation
This work addresses the need for precise and extensible object recognition in assistive robotics to enable safe navigation in diverse environments, representing a domain-specific incremental advancement.
The paper tackled the problem of open vocabulary 3D object detection for smart wheelchair navigation by proposing OpenNav, a zero-shot pipeline that integrates 2D detection with depth processing to create 3D bounding boxes, resulting in significant improvements on the Replica dataset with mAP25 (+9 points) and mAP50 (+5 points).
Open vocabulary 3D object detection (OV3D) allows precise and extensible object recognition crucial for adapting to diverse environments encountered in assistive robotics. This paper presents OpenNav, a zero-shot 3D object detection pipeline based on RGB-D images for smart wheelchairs. Our pipeline integrates an open-vocabulary 2D object detector with a mask generator for semantic segmentation, followed by depth isolation and point cloud construction to create 3D bounding boxes. The smart wheelchair exploits these 3D bounding boxes to identify potential targets and navigate safely. We demonstrate OpenNav's performance through experiments on the Replica dataset and we report preliminary results with a real wheelchair. OpenNav improves state-of-the-art significantly on the Replica dataset at mAP25 (+9pts) and mAP50 (+5pts) with marginal improvement at mAP. The code is publicly available at this link: https://github.com/EasyWalk-PRIN/OpenNav.