State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

Abstract

Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

“…With recent advancements in the VO systems with loop-closure, the boundary between VO and Visual-SLAM (V-SLAM) has become blurred; nevertheless, VO systems devote more attention to ego-motion estimation than to map building [ 157 ]. There exist several review papers on the VO systems—[ 13 , 158 , 159 , 160 ] provided a comprehensive overview of VO and V-SLAM; the two-part survey [ 161 , 162 ] highlighted feature-based VO; while [ 163 , 164 , 165 , 166 ] conducted reviews of recent advancements in VO and V-SLAM using state-of-the-art data-driven methods. VO can be classified into geometry-based methods and learning-based methods; geometry-based methods can be further categorized into feature-based approaches, appearance-based approaches, and hybrid approaches.…”

“…For mobile robot odometry—especially for Visual–Inertial Odometry (VIO)—two main approaches are used for sensor fusion, namely, the tightly coupled approach and the loosely coupled approach. In the loosely coupled approach, each sensor has its own estimator (e.g., VO and IMU), and the final result is a combination of each estimator, while in the tightly coupled approach the sensor measurements are directly fused in a single processor [ 166 ].…”

Passive Intermodulation at Contacts of Rough Conductors

“…They form a thermal node shown in Figure 5. Simultaneous solution of ( 5)- (7) gives an approximation of the local resistivity ( )…”

“…The continuously growing volume and speed of data transmission pose major challenges to existing and future wireless and satellite communications and navigation systems [1][2][3][4][5][6][7]. The stringent requirements for the integrity of information signals push the limits of radio frequency (RF) hardware.…”

“…The detrimental effect of PIM manifests itself in spurious emissions, increased noise and distortion of the original information signals. PIM products are particularly harmful to radars, wireless and space communications systems and radio astronomy [5][6][7]20,21].…”