Reliable long distance obstacle detection and motion planning is a key issue for modern intelligent vehicles, since it can help to make the decision early and design proper driving trajectory to avoid discomfort for the passengers caused by hard brake or sudden large lateral movement. Specifically, when there is vehicle parked on the roadside, we need to detect its position and pass it safely with proper distance without causing much disruption during driving. In this paper, we propose a method to detect roadside parked vehicles robustly and design a trajectory with proper lateral offset from the lane center for the host vehicle to safely pass by it. To successfully detect the roadside parked vehicles, we fuse the output from a long range lidar and radar. We pre-compute multiple path candidates with different lateral offset, and the path planner selects the most proper one based on the distance of the parked vehicle to the lane center. To deal with false alarms and missing detections, we apply temporal filtering to the detection output and history of the decision making. The speed control is carefully designed to ensure that the host vehicle passes the parked vehicle with a safe and comfortable speed. The implemented system was evaluated in numerous scenarios with vehicles parked on the roadside. The results show that the system effectively commands the host vehicle to pass by the parked vehicle safely and comfortably with proper distance and smooth trajectory.

This paper describes a real-time pre-visualization method using augmented reality techniques for videographers. It enables them to test camera work without real actors in a real environment. As a substitute for real actors, virtual ones are superimposed on a live video in real-time according to a real camera motion and an illumination condition. The key technique of this method is real-time motion estimation of a camera, which can be applied to unknown complex environments including natural objects. In our method, geometric and photometric registration problems for such unknown environments are solved to realize the above visualization. A prototype system demonstrates availability of the pre-visualization method.

This paper describes photometric registration for augmented reality (AR) using a high-dynamic-range (HDR) image. In photorealistic AR, estimating the lighting environment of virtual objects is difficult because of low dynamic range cameras. In order to overcome this problem, we propose a method that estimates the lighting environment from an HDR image and renders virtual objects using an HDR environment map. Virtual objects are overlaid in real-time by adjusting the dynamic range of the rendered image with tone mapping according to the exposure time of the camera. The HDR image is generated from multiple images captured with various exposure times. We have found through experimentation that the updating rate is improved by effectively limiting the dynamic range, depending on the exposure time. We have verified the effect of limiting the dynamic range on the reality of virtual objects.

This paper proposes an accurate geometric registration method by estimating blur effects from a degraded image with image markers for augmented reality. A small and inexpensive camera used in augmented reality systems sometimes captures degraded images because its focus and/or iris are fixed. This degradation of a captured image affects the accuracy of the detected positions of feature points in the image. The proposed method improves the accuracy of the estimated camera position and posture by estimating blur effects from the captured image, and by correcting the detected positions of feature points through the results. The effectiveness of the method is confirmed through experiments of corner estimation from simulated images and extrinsic camera parameter estimation from real images.

Photometric registration is as important as geometric registration to generate a seamless augmented reality scene. Especially the difference in image quality between a real image and virtual objects caused by defocusing and motion blurring in capturing a real scene image easily exhibits the seam between real and virtual worlds. To avoid this problem in video see-through augmented reality, it is necessary to simulate the optical system of camera when virtual objects are rendered. This paper proposes an image composition method for video see-through augmented reality, which is based on defocusing and motion blurring estimation from the captured real image and rendering of virtual objects with blur effects. In experiments, the effectiveness of the proposed method is confirmed by comparing a real image with virtual objects rendered by the proposed method.