Robust multi-sensor fusion for micro aerial vehicle navigation in GPS-degraded/denied environments

Chambers, Andrew; Scherer, Sebastian; Yoder, Luke; Jain, Sezal; Nuske, Stephen; Singh, Sanjiv

doi:10.1109/acc.2014.6859341

Cited by 33 publications

(27 citation statements)

References 21 publications

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“…State Estimation (UKF): We fuse stereo visual odometry, IMU measurements, and altitude measurements with an unscented Kalman filter. This approach is presented in detail in Chambers et al (2014).…”

Section: System Overviewmentioning

confidence: 99%

Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

Nuske

Choudhury

Jain

et al. 2015

Journal of Field Robotics

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Mapping a river's geometry provides valuable information to help understand the topology and health of an environment and deduce other attributes such as which types of surface vessels could traverse the river. While many rivers can be mapped from satellite imagery, smaller rivers that pass through dense vegetation are occluded. We develop a micro air vehicle (MAV) that operates beneath the tree line, detects and maps the river, and plans paths around three-dimensional (3D) obstacles (such as overhanging tree branches) to navigate rivers purely with onboard sensing, with no GPS and no prior map. We present the two enabling algorithms for exploration and for 3D motion planning. We extract high-level goal-points using a novel exploration algorithm that uses multiple layers of information to maximize the length of the river that is explored during a mission. We also present an efficient modification to the SPARTAN (Sparse Tangential Network) algorithm called SPARTANlite, which exploits geodesic properties on smooth manifolds of a tangential surface around obstacles to plan rapidly through free space. Using limited onboard resources, the exploration and planning algorithms together compute trajectories through complex unstructured and unknown terrain, a capability rarely demonstrated by flying vehicles operating over rivers or over ground. We evaluate our approach against commonly employed algorithms and compare guidance decisions made by our system to those made by a human piloting a boat carrying our system over multiple kilometers. We also present fully autonomous flights on riverine environments generating 3D maps over several hundred-meter stretches of tight winding rivers. C 2015 Wiley Periodicals, Inc.

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Section: System Overviewmentioning

confidence: 99%

Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

Nuske

Choudhury

Jain

et al. 2015

Journal of Field Robotics

Self Cite

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“…Therefore, a larger HSV threshold range is applied to increase the detection robustness. In our implementation with OpenCV, the HSV range for red color is from (165, 40, 50) to (179, 255, 255) and from (0, 40, 50) to (15,255,255). Contours are detected and analyzed in the binary image after segmentation.…”

Section: Visual Target Detectionmentioning

confidence: 99%

A lightweight autonomous MAV for indoor search and rescue

Lan

et al. 2019

Asian Journal of Control

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Micro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser-based SLAM module and a custom-designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor-outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree-based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award-winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM.

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“…Several approaches have been presented to work around these issues, such as simultaneously tracking a GPS-corrected and odometry-only global trajectory [1,31], or using a series of measurement gates [32]. Other approaches refrain from incorporating GPS into the filter at all, opting instead to incorporate GPS exclusively using a pose graph [33].…”

Section: Eventual Global Updatementioning

confidence: 99%

Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation

et al. 2018

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Robust multi-sensor fusion for micro aerial vehicle navigation in GPS-degraded/denied environments

Cited by 33 publications

References 21 publications

Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

A lightweight autonomous MAV for indoor search and rescue

Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation

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