3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction

A Real-time Range Finding System with Binocular Stereo Vision

To acquire range information for mobile robots, a TMS320DM642 DSP-based range finding system with binocular stereo vision is proposed. Firstly, paired images of the target are captured and a Gaussian filter, as well as improved Sobel kernels, are achieved. Secondly, a feature-based local stereo matching algorithm is performed so that the space location of the target can be determined. Finally, in order to improve the reliability and robustness of the stereo matching algorithm under complex conditions, the confidence filter and the left-right consistency filter are investigated to eliminate the mismatching points. In addition, the range finding algorithm is implemented in the DSP/BIOS operating system to gain real-time control. Experimental results show that the average accuracy of range finding is more than 99% for measuring single-point distances equal to 120cm in the simple scenario and the algorithm takes about 39ms for ranging a time in a complex scenario. The effectivity, as well as the feasibility, of the proposed range finding system are verified

Development of a video-rate range finder using dynamic threshold method for characteristic point detection

This study develops a video-rate stereo range finding circuit to obtain the depth of objects in a scene by processing video signals (R, G, B, and brightness signals) from binocular CCD cameras. The electronic circuit implements a dynamic threshold method to decrease the affect of signal noise in characteristic point detection, where a video signal from each CCD camera is compared with multiple thresholds, shifting dynamically by feeding back the previous comparison result. Several object depth measurement experiments for simple indoor scenes show that the dynamic threshold method gives high acquisition and correct rates of depth data compared with those by a fixed threshold method for the video signals and a relative method for R, G, and B signals utilized in the authors' previous range finders

Image-Aided Navigation Using Cooperative Binocular Stereopsis

This thesis proposes a novel method for cooperatively estimating the positions of two vehicles in a global reference frame based on synchronized image and inertial information. The proposed technique - cooperative binocular stereopsis - leverages the ability of one vehicle to reliably localize itself relative to the other vehicle using image data which enables motion estimation from tracking the three dimensional positions of common features. Unlike popular simultaneous localization and mapping (SLAM) techniques, the method proposed in this work does not require that the positions of features be carried forward in memory. Instead, the optimal vehicle motion over a single time interval is estimated from the positions of common features using a modified bundle adjustment algorithm and is used as a measurement in a delayed state extended Kalman filter (EKF). The developed system achieves improved motion estimation as compared to previous work and is a potential alternative to map-based SLAM algorithms

Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications

Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications