Zhiwei Zhu

Ten-fold Improvement in Visual Odometry Using Landmark Matching

Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction. Traditional visual odometry systems drift over time as the frame-to-frame errors accumulate. In this paper, we propose to improve visual odometry using visual landmarks in the scene. First, a dynamic local landmark tracking technique is proposed to track a set of local landmarks across image frames and select an optimal set of tracked local landmarks for pose computation. As a result, the error associated with each pose computation is minimized to reduce the drift significantly. Second, a global landmark based drift correction technique is proposed to recognize previously visited locations and use them to correct drift accumulated during motion. At each visited location along the route, a set of distinctive visual landmarks is automatically extracted and inserted into a landmark database dynamically. We integrate the landmark based approach into a navigation system with 2 stereo pairs and a low-cost inertial measurement unit (IMU) for increased robustness. We demonstrate that a real-time visual odometry system using local and global landmarks can precisely locate a user within 1 meter over 1000 meters in unknown indoor/outdoor environments with challenging situations such as climbing stairs, opening doors, moving foreground objects etc..

Stable vision-aided navigation for large-area augmented reality

In this paper, we present a unified approach for a drift-free and jitter-reduced vision-aided navigation system. This approach is based on an error-state Kalman filter algorithm using both relative (local) measurements obtained from image based motion estimation through visual odometry, and global measurements as a result of landmark matching through a pre-built visual landmark database. To improve the accuracy in pose estimation for augmented reality applications, we capture the 3D local reconstruction uncertainty of each landmark point as a covariance matrix and implicity rely more on closer points in the filter. We conduct a number of experiments aimed at evaluating different aspects of our Kalman filter framework, and show our approach can provide highly-accurate and stable pose both indoors and outdoors over large areas. The results demonstrate both the long term stability and the overall accuracy of our algorithm as intended to provide a solution to the camera tracking problem in augmented reality applications.

AR-mentor: Augmented reality based mentoring system

AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.

High-precision localization using visual landmarks fused with range data

Visual landmark matching with a pre-built landmark database is a popular technique for localization. Traditionally, landmark database was built with visual odometry system, and the 3D information of each visual landmark is reconstructed from video. Due to the drift of the visual odometry system, a global consistent landmark database is difficult to build, and the inaccuracy of each 3D landmark limits the performance of landmark matching. In this paper, we demonstrated that with the use of precise 3D Li-dar range data, we are able to build a global consistent database of high precision 3D visual landmarks, which improves the landmark matching accuracy dramatically. In order to further improve the accuracy and robustness, landmark matching is fused with a multi-stereo based visual odometry system to estimate the camera pose in two aspects. First, a local visual odometry trajectory based consistency check is performed to reject some bad landmark matchings or those with large errors, and then a kalman filtering is used to further smooth out some landmark matching errors. Finally, a disk-cache-mechanism is proposed to obtain the real-time performance when the size of the landmark grows for a large-scale area. A week-long real time live marine training experiments have demonstrated the high-precision and robustness of our proposed system.

Multi-modal sensor fusion algorithm for ubiquitous infrastructure-free localization in vision-impaired environments

In this paper, we present a unified approach for a camera tracking system based on an error-state Kalman filter algorithm. The filter uses relative (local) measurements obtained from image based motion estimation through visual odometry, as well as global measurements produced by landmark matching through a pre-built visual landmark database and range measurements obtained from radio frequency (RF) ranging radios. We show our results by using the camera poses output by our system to render views from a 3D graphical model built upon the same coordinate frame as the landmark database which also forms the global coordinate system and compare them to the actual video images. These results help demonstrate both the long term stability and the overall accuracy of our algorithm as intended to provide a solution to the GPS denied ubiquitous camera tracking problem under both vision-aided and vision-impaired conditions.

Virtual Insertion: Robust Bundle Adjustment over Long Video Sequences.

Our goal is to circumvent one of the roadblocks of using existing bundle adjustment algorithms for achieving satisfactory large-area structure from motion over long video sequences, namely, the need for sufficient visual features tracked across consecutive frames. We accomplish it by using a novel “virtual insertion” scheme, which constructs virtual points and virtual frames to adapt the existence of visual landmark link outage, namely “visual breaks” due to no common features observed from neighboring camera views in challenging environments. We show how to insert virtual point correspondences at each break position and its neighboring frames, by transforming initial motion estimations from non-vision sensors into 3D to 2D projection constraints of virtual scene landmarks. We also show how to add virtual frames to bridge the gap of nonoverlapping field of view (FOV) across sequential frames. Experiments are conducted on several real-world challenging video sequences, collected by multi-sensor based visual odometry systems. We demonstrate our proposed scheme significantly improves bundle adjustment performance in both drift correction and reconstruction accuracy.