Abstract
Micro Air Vehicles (MAVs) equipped with various sensors are able to carry out autonomous flights. However, the self-localization of autonomous agents is mostly dependent on Global Navigation Satellite Systems (GNSS). In order to provide an accurate navigation solution in absence of GNSS signals, this article presents a hybrid sensor. The hybrid sensor is a deep integration of a monocular camera and a 2D laser rangefinder so that the motion of the MAV is estimated. This realization is expected to be more flexible in terms of environments compared to laser-scan-matching approaches. The estimated ego-motion is then integrated in the MAV’s navigation system. However, first, the knowledge about the pose between both sensors is obtained by proposing an improved calibration method. For both calibration and ego-motion estimation, 3D-to-2D correspondences are used and the Perspective-3-Point (P3P) problem is solved. Moreover, the covariance estimation of the relative motion is presented. The experiments show very accurate calibration and navigation results.
Highlights
At present time, Micro Air Vehicles (MAVs) are becoming very popular in various applications.They are affordable and flexible in terms of location due to their size and weight
In order to correctly integrate relative measurements, the navigation system is augmented with the Stochastic Cloning Filter (SCF) approach [23]
The various aspects from calibration, to ego-motion estimation, to covariance estimation, to the final integrated navigation system are studied
Summary
Micro Air Vehicles (MAVs) are becoming very popular in various applications. Due to the geometry of the calibration object, 3D feature points of the laser rangefinder are recovered in image coordinates The pose between both sensors is obtained by solving the Perspective-3-Point (P3P) problem [10]. The authors of [11] propose an approach that estimates the depth information of image features, which do not necessarily lie on the projected laser line This is possible by assuming ground vehicle motion and a structured environment, such as indoors. The objective is to obtain an accurate navigation solution and become more flexible in terms of the MAV’s surroundings compared to the existing laser-scan-matching approach [4] This includes many aspects from the deep integration of two complementary sensors, to ego-motion estimation, to the final integrated navigation system. This results in a more accurate and robust navigation solution
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