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

A High Spatial Resolution Mid-Infrared Spectroscopic Study of the Nuclei and Star-Forming Regions in Luminous Infrared Galaxies

We present a high spatial (diffraction-limited) resolution (~0.3") mid-infrared (MIR) spectroscopic study of the nuclei and star-forming regions of 4 local luminous infrared galaxies (LIRGs) using T-ReCS on the Gemini South telescope. We investigate the spatial variations of the features seen in the N-band spectra of LIRGs on scales of ~100 pc, which allow us to separate the AGN emission from that of the star formation (SF). We compare our Gemini T-ReCS nuclear and integrated spectra of LIRGs with those obtained with Spitzer IRS. The 9.7um silicate absorption feature is weaker in the nuclei of the LIRGs than in the surrounding regions. This is probably due to the either clumpy or compact environment of the central AGN or young, nuclear starburst. We find that the [NeII] luminosity surface density is tightly and directly correlated with that of Pa-alpha for the LIRG star-forming regions (slope of 1.00+-0.02). Although the 11.3um PAH feature shows also a trend with Pa-alpha, this is not common for all the regions. We also find that the [NeII]\Pa-alpha ratio does not depend on the Pa-alpha equivalent width (EW), i.e., on the age of the ionizing stellar populations, suggesting that, on the scales probed here, the [NeII] emission line is a good tracer of the SF activity in LIRGs. On the other hand, the 11.3um PAH\Pa-alpha ratio increases for smaller values of the Pa-alpha EW (increasing ages), indicating that the 11.3um PAH feature can also be excited by older stars than those responsible for the Pa-alpha emission. Additional high spatial resolution observations are essential to investigate, in a statistical way, the star formation in local LIRGs at the smallest scales and to probe ultimately whether they share the same physical properties as high-z LIRGs, ULIRGs and submillimiter galaxies.Comment: 23 pages (apjstyle), 19 figures, accepted for publicacion in Ap

Airborne Navigation by Fusing Inertial and Camera Data

Unmanned aircraft systems (UASs) are often used as measuring system. Therefore, precise knowledge of their position and orientation are required. This thesis provides research in the conception and realization of a system which combines GPS-assisted inertial navigation systems with the advances in the area of camera-based navigation. It is presented how these complementary approaches can be used in a joint framework. In contrast to widely used concepts utilizing only one of the two approaches, a more robust overall system is realized. The presented algorithms are based on the mathematical concepts of rigid body motions. After derivation of the underlying equations, the methods are evaluated in numerical studies and simulations. Based on the results, real-world systems are used to collect data, which is evaluated and discussed. Two approaches for the system calibration, which describes the offsets between the coordinate systems of the sensors, are proposed. The first approach integrates the parameters of the system calibration in the classical bundle adjustment. The optimization is presented very descriptive in a graph based formulation. Required is a high precision INS and data from a measurement flight. In contrast to classical methods, a flexible flight course can be used and no cost intensive ground control points are required. The second approach enables the calibration of inertial navigation systems with a low positional accuracy. Line observations are used to optimize the rotational part of the offsets. Knowledge of the offsets between the coordinate systems of the sensors allows transforming measurements bidirectional. This is the basis for a fusion concept combining measurements from the inertial navigation system with an approach for the visual navigation. As a result, more robust estimations of the own position and orientation are achieved. Moreover, the map created from the camera images is georeferenced. It is shown how this map can be used to navigate an unmanned aerial system back to its starting position in the case of a disturbed or failed GPS reception. The high precision of the map allows the navigation through previously unexplored area by taking into consideration the maximal drift for the camera-only navigation. The evaluated concept provides insight into the possibility of the robust navigation of unmanned aerial systems with complimentary sensors. The constantly increasing computing power allows the evaluation of big amounts of data and the development of new concept to fuse the information. Future navigation systems will use the data of all available sensors to achieve the best navigation solution at any time

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community