We present an efficient path planner for smart wheelchairs based on harmonic potential fields. While the use of harmonic fields can always guarantee finding an existing path, they are extremely computational intensive and a sufficiently detailed map of the environment may lead to an unfeasible solution for the path. Also, since our target application is for the navigation of a smart wheelchair, for people with severe disabilities, the path provided by the harmonic field is frequently too sharp and needs to be smoothened. In order to address the first problem, we propose a parallel algorithm implemented using Graphics Processor Units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. And for the second problem, we developed a rubber band model that provides extra forces to be added to the attracting forces of the harmonic fields. This model assumes that the path is an elastic line, a rubber band, connecting the source and destination points. This rubber band simulates the internal tension forces trying to tighten the line. As the result section demonstrates, both the original path from the harmonic field alone and the path smoothened by the rubber band model have approximate the same length, but the first path contains many bumps, sharp angles, and zigzags , while the second one provides a much more comfortable ride for the passenger of the wheelchair. Either one is executed in real-time, allowing the proposed method to be used for real navigation of smart wheelchairs.
Abstract-This paper presents a method for multi-view 3D modeling of human bodies using virtual stereopsis. The algorithm expands and improves the method used in [5], but unlike that method, our approach does not require multiple calibrated cameras and/or carefully-positioned turn tables. Instead, an algorithm using SIFT feature extraction is employed and an accurate motion estimation is performed to calculate the position of virtual cameras around the object. That is, by employing a single pair of cameras mounted on a same tripod, our algorithm computes the relative pose between camera and object and creates virtual cameras from the consecutive images in the video sequence. Besides not requiring any special setup, another advantage of our method is in the simplicity to obtain denser models if necessary: by only increasing the number of sampled images during the object-camera motion. As the quantitative results presented here demonstrate, our method compares to the PMVS method, while it makes it much simpler and cost-effective to implement.
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