Autonomous driving paper index
A smart CMOS camera for autonomous navigation systems
One-line summary
In this thesis I present my research into the implementation of an edge point detection algorithm within a Smart CMOS Camera.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In this thesis I present my research into the implementation of an edge point detection algorithm within a Smart CMOS Camera. The research includes the development and implementation of a new edge detection algorithm. The algorithm was designed for implementation in a Near Sensor Image Processing (NSIP) structure. This structure was integrated onto a CMOS substrate alongside a random access image-sensing array. The random access array employed pixels with integral gain. Operational specifications for the Smart CMOS Camera were derived from the spatial resolution, the frame rate and edge acuity, necessary to implement corridor autonomous navigation at a walking pace of lm/s. The architecture used to implement NSIP structure is referred to as the Scanned Layer Architecture (SLA). This reflects the layered processing adopted to overcome the connection restrictions of the CMOS substrate. The new edge detector was labelled as the SLA detector. This detector was developed from a study of the gradient based edge detection algorithms. Its integration into a mixed signal CMOS processor was facilitated by limiting the spatial derivative convolution coefficients to integer values, and by minimising the number of product terms. The SLA edge detector was designed to retain edge sense and edge direction information. Two directional edge sets were exported from each processed image. These were a vertical edge set and a horizontal edge set. Within these sets the edge information was encoded in a 3-stateformat to retain the edge sense information. A new edge point metric was developed for the quantitative assessment of the SLA algorithm results.This allowed the detector to be assessed against the requirements of a vision based navigation algorithm. Simulation results demonstrate the use of the SLA edge data to locate a robot's floor position within a corridor environment.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments