Automotive Research & Testing Center

Yu-Fang Wang

Yi-Shueh Tsai

Li-You Hsu

Yi-Jie Lin

Cheng-Kai Wang

A feature point integration positioning system includes a moving object, an image input source, an analyzing module and a positioning module. The image input source is disposed at the moving object to shoot an environment for obtaining a sequential image dataset. The analyzing module includes a machine vision detecting unit configured to generate a plurality of first feature points in each of the images based on each of the images, a deep learning detecting unit configured to generate a plurality of second feature points in each of the images based on each of the images, and an integrating unit configured to integrate the first feature points and the second feature points in each of the images into a plurality of integrated feature points in each of the images. The positioning module confirms a position of the moving object relative to the environment at each of the time points.

Feature point integration positioning system, feature point integration positioning method and non-transitory computer-readable memory

A method of simultaneous localization and mapping (SLAM) is provided to position a target object. Each of detected tracked objects in a surrounding environment of the target object is classified into a moving object or a static object based on data detected at different time points. The target object is then positioned without considering any of the tracked objects that are classified into a moving object.

Method of simultaneous localization and mapping

A lane stripe detecting method based on a three-dimensional LIDAR is for detecting a lane stripe of a road surface around a vehicle. A data acquisition transforming step is for obtaining a plurality of vehicle scan point coordinates. The vehicle scan point coordinates are divided into a plurality of scan lines, and each of the scan lines has a plurality of scan points. A horizontal layer lane stripe judging step is judging whether each of the scan points is a horizontal stripe point or a non-horizontal stripe point according to a threshold value of each of the scan lines. At least two of the threshold values of the scan lines are different from each other. A vertical layer lane stripe judging step is for judging whether each of the horizontal stripe points is a same lane stripe point or a different lane stripe point.

Lane stripe detecting method based on three-dimensional lidar and system thereof

A dynamic road surface detecting method based on a three-dimensional sensor is provided. The three-dimensional sensor receives a plurality of laser-emitting points reflected from a road surface to generate a plurality of three-dimensional sensor scan point coordinates which is transmitted to a point cloud processing module. The point cloud processing module transforms the three-dimensional sensor scan point coordinates to a plurality of vehicle scan point coordinates according to a coordinate translation equation, and then transforms a plurality of vehicle coordinate height values of the vehicle scan point coordinates to a road surface height reference line according to a folding line fitting algorithm. An absolute difference of two scan point height values of any two adjacent scan points on each of the scan lines is analyzed to generate a discontinuous point. The point cloud processing module links the discontinuous points to form a road boundary.

Growing community of inventors

Changhua County, Taiwan

Yu-Fang Wang