Sijong Kim

The Development of Sensor System and 3D World Modeling for Autonomous Vehicle

This paper describes a novel sensor system for 3D world modeling of an autonomous vehicle in large-scale outdoor environments. When an autonomous vehicle performs path planning and path following, well-constructed 3D world model of target environment is very important for analyze the environment and track the determined path. To generate well-construct 3D world model, we develop a novel sensor system. The proposed novel sensor system consists of two 2D laser scanners, two single cameras, a DGPS (Differential Global Positioning System) and an IMU (Inertial Measurement System). We verify the effectiveness of the proposed sensor system through experiment in large-scale outdoor environment.

Moving Region Segmentation Using Sparse Motion Cue from a Moving Camera

This paper presents a method for pixel-wise segmentation of moving regions using sparse motion cues on an image from a freely moving camera. The main idea is to utilize residual motion, i.e., motion relative to a background, on sparse grid points. Our algorithm consists of three parts: global motion estimation, characterization of points based on sparse motion cue, and pixel-wise labeling of moving regions. Experimental results on real image sequences are presented, showing the effectiveness of the proposed method.

Online multi-target tracking using non-linear motion estimation

This paper deals with an online multi-target tracking method applicable to real world applications such as surveillance systems, traffic monitoring, and driving assistance systems. The proposed method consists of three stages, non-linear motion estimation in continuous time domain, hypothesis generation, data association. We tested the proposed method on publicly available video sequences and analyzed the results of the proposed method.

A practical 6D robot pose estimation using GPS and IMU in outdoor

The aim of this study is to estimate 6D robot pose. We estimate 6D robot pose using GPS (Global IMU(Inertial Measurement System) in outdoor. We proposed reliability method for 6D robot pose. We get 3D mapping results and compare the 3D mapping results with and without our method. The 3D mapping results with our method was more accurate.

Building a mobile platform for spatiotemporal integration of 3D outdoor world models

This paper introduces our mobile platform, which equips multiple sensors for 3D outdoor world modeling. The data from GPS and IMU on the platform are fused to provide vehicle pose on the ground. The laterally installed LRFs(Laser Range Finder) on each side of the vehicle give environment perception information. The data from the LRFs are fused with cameras in order to make colored LRF points. With these multiple sensors, we have successfully built spatiotemporal integrated 3D world models of outdoor environments.

Probabilistic voxel mapping using stereo matching confidence

There has been meaningful research into the development of 3D world modeling techniques that are important requisite for intelligent vehicle navigation. In this paper we describe a 3D probabilistic voxel mapping process using stereo matching confidence. Proposed 3D probabilistic voxel map is more reliable representation than general voxel map that just contains the occupancy information. To get the matching confidence value, we evaluate stereo matching costs and its propagation. We test the proposed method in large-scale outdoor environment.