scholarly journals Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4287 ◽  
Author(s):  
Euntae Hong ◽  
Jongwoo Lim
Keyword(s):  
Visual Information ◽  
Inertial Measurement Unit ◽  
High Accuracy ◽  
Measurement Unit ◽  
Optimization Framework ◽  
Vehicle Systems ◽  
Motional State ◽  
Aerial Vehicle ◽  
Linear Problems ◽  
Inertial Measurements

Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework and the stable initialization of scale and gravity using relative pose constraints. To account for the ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the European Robotics Challenge (EuRoC) dataset verify the efficacy and accuracy of the proposed method.

scholarly journals Visual Inertial Odometry with Robust Initialization and Online Scale Estimation

2018 ◽  
Author(s):  
Euntae Hong ◽  
Jongwoo Lim
Keyword(s):  
Visual Information ◽  
Inertial Measurement Unit ◽  
High Accuracy ◽  
Measurement Unit ◽  
Optimization Framework ◽  
Vehicle Systems ◽  
Motional State ◽  
Aerial Vehicle ◽  
Linear Problems ◽  
Inertial Measurements

Visual inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework, and the stable initialization of scale and gravity using relative pose constraints. To count for ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the EuRoC dataset verify the efficacy and accuracy of the proposed method.

scholarly journals Online Spatial and Temporal Calibration for Monocular Direct Visual-Inertial Odometry

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2273
Author(s):  
Zheyu Feng ◽  
Jianwen Li ◽  
Lundong Zhang ◽  
Chen Chen
Keyword(s):  
Inertial Measurement Unit ◽  
Prior Information ◽  
Measurement Unit ◽  
Online Calibration ◽  
The Public ◽  
Temporal Parameters ◽  
Novel Approach ◽  
Aerial Vehicle ◽  
Feature Based ◽  
Temporal Offset

Owing to the nonlinearity in visual-inertial state estimation, sufficiently accurate initial states, especially the spatial and temporal parameters between IMU (Inertial Measurement Unit) and camera, should be provided to avoid divergence. Moreover, these parameters are required to be calibrated online since they are likely to vary once the mechanical configuration slightly changes. Recently, direct approaches have gained popularity for their better performance than feature-based approaches in little-texture or low-illumination environments, taking advantage of tracking pixels directly. Based on these considerations, we perform a direct version of monocular VIO (Visual-inertial Odometry), and propose a novel approach to initialize the spatial-temporal parameters and estimate them with all other variables of interest (IMU pose, point inverse depth, etc.). We highlight that our approach is able to perform robust and accurate initialization and online calibration for the spatial and temporal parameters without utilizing any prior information, and also achieves high-precision estimates even when large temporal offset occurs. The performance of the proposed approach was verified through the public UAV (Unmanned Aerial Vehicle) dataset.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

Cartesian Control for the Inertial Measurement Unit Calibration Platform

2000 ◽  
Author(s):  
John J. Hall ◽  
Robert L. Williams ◽  
Frank van Graas
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Vertical Motion ◽  
High Accuracy ◽  
Measurement Unit ◽  
Positioning System ◽  
Ohio University ◽  
Inertial Measurement ◽  
Gps Technology ◽  
Global Positioning

Abstract The Department of Mechanical Engineering and the Avionics Engineering Center at Ohio University are developing an electromechanical system for the calibration of an inertial measurement unit (IMU) using global positioning system (GPS) antennas. The GPS antennas and IMU are mounted to a common platform to be oriented in the angular roll, pitch, and yaw motions. Vertical motion is also included to test the systems in a vibrational manner. A four-dof system based on the parallel Carpal Wrist is under development for this task. High-accuracy positioning is not required from the platform since the GPS technology provides absolute positioning for the IMU calibration process.

scholarly journals Sensitivity to Time Delays in VDM-Based Navigation

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 11 ◽  
Author(s):  
Gabriel Laupré ◽  
Mehran Khaghani ◽  
Jan Skaloud
Keyword(s):  
Time Delays ◽  
Inertial Measurement Unit ◽  
Time Frame ◽  
Satellite System ◽  
Measurement Unit ◽  
Control Input ◽  
Aerial Vehicle ◽  
Aerial Platforms ◽  
Global Navigation Satellite ◽  
Vehicle Dynamic Model

A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit (IMU) data and all other observations when available (e.g., global navigation satellite system (GNSS) position, barometric altitude, etc.). This study analyzes the (non-) tolerances of possible delays in control-input command with respect to navigation performance on a fixed-wing unmanned aerial vehicle (UAV). Multiple simulations using two emulated trajectories based on real flights reveal the vital importance of correct time-tagging of servo data while that of motor data turned out to be tolerable to a considerably large extent.

scholarly journals The Visual–Inertial Canoe Dataset

2018 ◽  
Vol 37 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Martin Miller ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Ground Truth ◽  
Data Bank ◽  
Measurement Unit ◽  
Round Trip ◽  
Inertial Measurement ◽  
Global Positioning ◽  
Position Data ◽  
Inertial Measurements

We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an inertial measurement unit (IMU), and a global positioning system (GPS) device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering a 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The dataset is stored on the Illinois Data Bank and can be accessed at: https://doi.org/10.13012/B2IDB-9342111_V1 .

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