Open Collections

Thukral, Shivam

University of British Columbia

A smart wheelchair improves the quality of life for older adults by supporting their mobility independence. Some critical maneuvering tasks, like table docking and doorway passage, can be challenging for older adults in wheelchairs, especially those with additional impairment of cognition, perception or fine motor skills. Supporting such functions in a shared manner with robot control seems to be an ideal solution. Considering this, we propose to augment smart wheelchair perception with the capability to identify potential docking locations in indoor scenes. ApproachFinder-CV is a computer vision pipeline that detects safe docking poses and estimates their desirability weight based on hand-selected geometric relationships and visibility. Although robust, this pipeline is computationally intensive. We leverage this vision pipeline to generate ground truth labels used to train an end-to-end differentiable neural net that is 15x faster. ApproachFinder-NN is a point-based method that draws motivation from Hough voting and uses deep point cloud features to vote for potential docking locations. Both approaches rely on just geometric information, making them invariant to image distortions. A large-scale indoor object detection dataset, SUN RGB-D, is used to design, train and evaluate the two pipelines. Potential docking locations are encoded as a 3D temporal desirability cost map that can be integrated into any real-time path planner. As a proof of concept, we use a model predictive controller that consumes this 3D costmap with efficiently designed task-driven cost functions to share human intent. This controller outputs a nominal path that is safe, goal-oriented and jerk-free for wheelchair navigation.

Text

2022-02-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80782

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/