Thermal-Inertial Odometry for Autonomous Flight Throughout the Night

Delaune, Jeff; Hewitt, Robert A.; Lytle, Laura; Sorice, Cristina; Thakker, Rohan; Matthies, Larry

doi:10.1109/iros40897.2019.8968238

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…Moreover, a multi-modality framework has proposed to cope with difficulties in percepting the environment around the vehicle at low visibility. In such frameworks, the robot's pose was estimated by using a multi-sensory data fusion technique, i.e., thermal imager with IMU [14], [201], eventbased camera with IMU [196], [197], [221], thermal imager and LiDAR measurements [16], Radar and LiDAR [214], and more than two sensory data [199], [200], [219].…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…DL approaches have the ability to adapt with inherited system nonlinearities as well as the variation in the environment. In addition to that, very limited studies have employed thermal camera for odometry estimation and overcoming its low feature resolution [14], [199]- [201]. 2) Applicability: Some platforms are limited with the power and computational capabilities.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…Therefore, in this case, several computer vision algorithms would not be effective and hence, would need further developments. In literature, very limited studies have been proposed related to TIO, such as [14], [199]- [201], and could be further investigated to overcome the limitations of thermal imagers. Moreover, [202] provides an approach employing LiDAR for a better visualization under different degraded environments.…”

Section: Localization Techniques In Low-visibility Environmentsmentioning

confidence: 99%

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

2021

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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.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Localization Techniques In Low-visibility Environmentsmentioning

confidence: 99%

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

2021

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“…This enables SLAM updates for features which are included in the state vector [31]. Features which are not included in the state vector are processed using MSCKF [10].…”

Section: B Visual Updatementioning

confidence: 99%

Range-Visual-Inertial Odometry: Scale Observability Without Excitation

Delaune

Bayard

2021

IEEE Robot. Autom. Lett.

Self Cite

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Traveling at constant velocity is the most efficient trajectory for most robotics applications. Unfortunately without accelerometer excitation, monocular Visual-Inertial Odometry (VIO) cannot observe scale and suffers severe error drift. This was the main motivation for incorporating a 1D laser range finder in the navigation system for NASA's Ingenuity Mars Helicopter. However, Ingenuity's simplified approach was limited to flat terrains. The current paper introduces a novel range measurement update model based on using facet constraints. The resulting range-VIO approach is no longer limited to flat scenes, but extends to any arbitrary structure for generic robotic applications. An important theoretical result shows that scale is no longer in the right nullspace of the observability matrix for zero or constant acceleration motion. In practical terms, this means that scale becomes observable under constant-velocity motion, which enables simple and robust autonomous operations over arbitrary terrain. Due to the small range finder footprint, range-VIO retains the minimal size, weight, and power attributes of VIO, with similar runtime. The benefits are evaluated on real flight data representative of common aerial robotics scenarios. Robustness is demonstrated using indoor stress data and fullstate ground truth. We release our software framework, called xVIO, as open source.

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“…By contrast, thermalinfrared images can effectively improve visibility in these scenarios. For example, J. Delaune [21] proposed a SLAM algorithm using thermal-infrared images, enabling autonomous flight of UAVs (Unmanned Aerial Vehicle) at night. c) Event cameras: Event cameras measure changes in the brightness of pixels which are with low delay, low power consumption, and high dynamic measurement range.…”

Section: A Slam With Different Sensorsmentioning

confidence: 99%

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

Yin¹,

Li²,

Li³

et al. 2021

Preprint

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We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals. All those sensors were well-calibrated and synchronized, and their data were recorded simultaneously. The ground truth trajectories were obtained by the motion capture device, a laser 3D tracker, and an RTK receiver. The dataset comprises 36 sequences (about 1TB) captured in diverse scenarios including both indoor and outdoor environments. We evaluate state-of-the-art SLAM algorithms on M2DGR. Results show that existing solutions perform poorly in some scenarios. For the benefit of the research community, we make the dataset and tools public. The webpage of our project is https://github.com/SJTU-ViSYS/M2DGR.

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Thermal-Inertial Odometry for Autonomous Flight Throughout the Night

Cited by 31 publications

References 27 publications

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

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

Range-Visual-Inertial Odometry: Scale Observability Without Excitation

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

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