Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

On Designing Tattoo Registration and Matching Approaches in the Visible and SWIR Bands

Face, iris and fingerprint based biometric systems are well explored areas of research. However, there are law enforcement and military applications where neither of the aforementioned modalities may be available to be exploited for human identification. In such applications, soft biometrics may be the only clue available that can be used for identification or verification purposes. Tattoo is an example of such a soft biometric trait. Unlike face-based biometric systems that used in both same-spectral and cross-spectral matching scenarios, tattoo-based human identification is still a not fully explored area of research. At this point in time there are no pre-processing, feature extraction and matching algorithms using tattoo images captured at multiple bands. This thesis is focused on exploring solutions on two main challenging problems. The first one is cross-spectral tattoo matching. The proposed algorithmic approach is using as an input raw Short-Wave Infrared (SWIR) band tattoo images and matches them successfully against their visible band counterparts. The SWIR tattoo images are captured at 1100 nm, 1200 nm, 1300 nm, 1400 nm and 1500 nm. After an empirical study where multiple photometric normalization techniques were used to pre-process the original multi-band tattoo images, only one was determined to significantly improve cross spectral tattoo matching performance. The second challenging problem was to develop a fully automatic visible-based tattoo image registration system based on SIFT descriptors and the RANSAC algorithm with a homography model. The proposed automated registration approach significantly improves the operational cost of a tattoo image identification system (using large scale tattoo image datasets), where the alignment of a pair of tattoo images by system operators needs to be performed manually. At the same time, tattoo matching accuracy is also improved (before vs. after automated alignment) by 45.87% for the NIST-Tatt-C database and 12.65% for the WVU-Tatt database

Non-iterative RGB-D-inertial Odometry

This paper presents a non-iterative solution to RGB-D-inertial odometry system. Traditional odometry methods resort to iterative algorithms which are usually computationally expensive or require well-designed initialization. To overcome this problem, this paper proposes to combine a non-iterative front-end (odometry) with an iterative back-end (loop closure) for the RGB-D-inertial SLAM system. The main contribution lies in the novel non-iterative front-end, which leverages on inertial fusion and kernel cross-correlators (KCC) to match point clouds in frequency domain. Dominated by the fast Fourier transform (FFT), our method is only of complexity $\mathcal{O}(n\log{n})$, where $n$ is the number of points. Map fusion is conducted by element-wise operations, so that both time and space complexity are further reduced. Extensive experiments show that, due to the lightweight of the proposed front-end, the framework is able to run at a much faster speed yet still with comparable accuracy with the state-of-the-arts