Abstract
Autonomous micro aerial vehicles (MAVs) equipped with onboard sensors, are idea platforms for missions in complex and confined environments for its low cost, small size and agile maneuver. Due to the size, power, weight and computation constraints inherent in the filed of MAVs, monocular visual-inertial system that consist of one camera and an inertial measurement (IMU) are the most suitable sensor suit for MAVs. In this paper, we proposed a monocular visual-inertial algorithm for estimating the state of a MAV. Firstly, the Semi-Direct Visual Odometry (SVO) algorithm used as the vision front-end of our framework was modified so that it can be used for forward-looking camera case. Second, an Error-state Kalman Filter was designed so that it can fuse the output of the SVO and IMU data to estimate the full state of the MAVs. We evaluated the proposed method with EuRoc Dataset and compare the results to the state-of-the-art visual-inertial algorithm, VINS-Mono. Experiments show that our estimator can achieve comparable accurate results.
Highlights
micro aerial vehicles (MAVs) are ideal platforms for missions such as exploration, inspecting, search and rescue in complex and confined environments due to its low cost, small size and agile maneuver
The dataset are collected on-board a Micro Aerial Vehicle (MAV), which contain stereo images(Aptina MT9V034 global shutter, WVGA monochrome, 2 × 20 FPS), synchronized inertial measurement unit (IMU) measurements(ADIS16448, angular rate and acceleration, 200 Hz), and accurate ground-truth states
The results do not contain the comparison of the performance difference between the original version of Semi-Direct Visual Odometry (SVO) and the our modified SVO as the the original version of SVO cannot run on this dataset
Summary
MAVs are ideal platforms for missions such as exploration, inspecting, search and rescue in complex and confined environments due to its low cost, small size and agile maneuver. To achieve these goals, it is essential that the MAVs is capable of autonomous navigation in unknown environments, which include reliable state estimation, control, environment mapping, planning and obstacles avoidance. As the platform becomes smaller, a monocular visualinertial navigation system (VINS), consisting of only a low-cost inertial measurement unit (IMU) and a camera, becomes the only viable sensor suite allowing autonomous flights with sufficient environmental awareness [4]. One contribution of this paper is that we modified the original SVO algorithm such that it can be used in the forward-looking camera case.
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