LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

Cao, Muqing; Yuan, Shenghai; Lyu, Yang; Nguyen, Thien‐Minh; Xie, Lihua

doi:10.48550/arxiv.2010.13072

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…To further improve the accuracy, techniques have been presented which combine vision and LIDAR measurement as in LIDAR-monocular visual odometry (LIMO) [ 176 ] and LVI-SLAM [ 177 ], combining monocular image tracking with precise depth estimates from LIDAR measurements for motion estimation. Methods such as LIRO [ 178 ] and VIRAL-SLAM [ 179 ] couple additional measurements such as ultrawide band (UWB) with visual and IMU sensors for robust pose estimation and map building. Other methods such as HDL-SLAM [ 180 ] and LIO-SAM [ 181 ] tightly couple IMU, LIDAR, and GPS measurements for globally consistent maps.…”

Section: Accumulated Situational Comprehensionmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Schmidt³

et al. 2023

Sensors

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The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

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Section: Accumulated Situational Comprehensionmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Schmidt³

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…With the development of more advanced 3D sensing technologies, navigation methods based on 3D sensors, such as stereo-vision [16], LiDAR [17], and RGB-D camera [18], can directly utilize 3D points with metric information in the estimation process, therefore can provide improved localization and reconstruction results. In addition to the most commonly used point features, line and plane features are also adopted as additional features in some particular mission scenarios, such as indoor servicing [19], [20], structure inspection [21], [22], and autonomous landing [23], in case that point features are not sufficient.…”

Section: Related Workmentioning

confidence: 99%

SPINS: Structure Priors aided Inertial Navigation System

Lyu¹,

Liu

Cao³

et al. 2020

Preprint

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Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robustness. More importantly, we incorporate general prior map information into the SLAM to restrain its drift and improve the accuracy. To avoid rigorous association between prior information and local observations, we parameterize the prior knowledge as low dimensional structural priors defined as relative distances/angles between different geometric primitives. The localization is formulated as a graphbased optimization problem that contains sliding-window-based variables and factors, including IMU, heterogeneous features, and structure priors. We also derive the analytical expressions of Jacobians of different factors to avoid the automatic differentiation overhead. To further alleviate the computation burden of incorporating structural prior factors, a selection mechanism is adopted based on the so-called information gain to incorporate only the most effective structure priors in the graph optimization. Finally, the proposed framework is extensively tested on synthetic data, public datasets, and, more importantly, on the real UAV flight data obtained from a building inspection task. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

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LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

Cited by 2 publications

References 33 publications

From SLAM to Situational Awareness: Challenges and Survey

From SLAM to Situational Awareness: Challenges and Survey

SPINS: Structure Priors aided Inertial Navigation System

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