A comparison of SWaP-limited, visual-inertial odometry systems for GPS-denied navigation

Mise, Olegs; Madison, Richard; Haight, Brian

doi:10.1117/12.2554456

Cited by 2 publications

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“…This experiment aims to analyze the behavior of the proposed strategy and the Intel RealSense Tracking Camera T265 tracking sensor. Specific information about its sensor can be obtained at [34], [35].…”

Section: Object Trackingmentioning

confidence: 99%

DeepSpatial: Intelligent Spatial Sensor to Perception of Things

et al. 2021

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This paper discusses a spatial sensor to identify and track objects in the environment. The sensor is composed of an RGB-D camera that provides point cloud and RGB images and an egomotion sensor able to identify its displacement in the environment. The proposed sensor also incorporates a data processing strategy developed by the authors to conferring to the sensor different skills. The adopted approach is based on four analysis steps: egomotive, lexical, syntax, and prediction analysis. As a result, the proposed sensor can identify objects in the environment, track these objects, calculate their direction, speed, and acceleration, and also predict their future positions. The on-line detector YOLO is used as a tool to identify objects, and its output is combined with the point cloud information to obtain the spatial location of each identified object. The sensor can operate with higher precision and a lower update rate, using YOLOv2, or with a higher update rate, and a smaller accuracy using YOLOv3-tiny. The object tracking, egomotion, and collision prediction skills are tested and validated using a mobile robot having a precise speed control. The presented results show that the proposed sensor (hardware + software) achieves a satisfactory accuracy and usage rate, powering its use to mobile robotic. This paper's contribution is developing an algorithm for identifying, tracking, and predicting the future position of objects embedded in a compact hardware. Thus, the contribution of this paper is to convert raw data from traditional sensors into useful information.

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Section: Object Trackingmentioning

confidence: 99%

DeepSpatial: Intelligent Spatial Sensor to Perception of Things

et al. 2021

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“…Also, there exist robust commercial implementations. For example, Intel released in 2019 the RealSense T265 Tracking Camera, a light and cheap device (less than $200) with two fisheye lens sensors, an Inertial Measurement Unit (IMU) and a Visual Processing Unit (VPU), which executes a visual Simultaneous Localization and Mapping (SLAM) algorithm [13]. Thus, this device is an adequate visual odometer for small drones.…”

Section: Introductionmentioning

confidence: 99%

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

et al. 2021

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Drones may be more advantageous than fixed cameras for quality control applications in industrial facilities, since they can be redeployed dynamically and adjusted to production planning. The practical scenario that has motivated this paper, image acquisition with drones in a car manufacturing plant, requires drone positioning accuracy in the order of 5 cm. During repetitive manufacturing processes, it is assumed that quality control imaging drones will follow highly deterministic periodic paths, stop at predefined points to take images and send them to image recognition servers. Therefore, by relying on prior knowledge about production chain schedules, it is possible to optimize the positioning technologies for the drones to stay at all times within the boundaries of their flight plans, which will be composed of stopping points and the paths in between. This involves mitigating issues such as temporary blocking of line-of-sight between the drone and any existing radio beacons; sensor data noise; and the loss of visual references. We present a self-corrective solution for this purpose. It corrects visual odometer readings based on filtered and clustered Ultra-Wide Band (UWB) data, as an alternative to direct Kalman fusion. The approach combines the advantages of these technologies when at least one of them works properly at any measurement spot. It has three method components: independent Kalman filtering, data association by means of stream clustering and mutual correction of sensor readings based on the generation of cumulative correction vectors. The approach is inspired by the observation that UWB positioning works reasonably well at static spots whereas visual odometer measurements reflect straight displacements correctly but can underestimate their length. Our experimental results demonstrate the advantages of the approach in the application scenario over Kalman fusion, in terms of stopping point detection and trajectory estimation error.INDEX TERMS Ultra-Wide Band, visual odometer, sensor fusion, wireless technologies, drone positioning, industry applications, Industry 4.0, quality control.

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A comparison of SWaP-limited, visual-inertial odometry systems for GPS-denied navigation

Cited by 2 publications

References 0 publications

DeepSpatial: Intelligent Spatial Sensor to Perception of Things

DeepSpatial: Intelligent Spatial Sensor to Perception of Things

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

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