Abstract
An indoor navigation system based on stereo camera and inertial sensors with points and lines is proposed to further improve the accuracy and robustness of the navigation system in complex indoor environments. The point and line features, which are fast extracted by ORB method and line segment detector (LSD) method, are both employed in this system to improve its ability to adapt to complex environments. In addition, two different representations of lines are adopted to improve the efficiency of the system. Besides stereo camera, an inertial measurement unit (IMU) is also used in the system to further improve its accuracy and robustness. An estimator is designed to integrate the camera and IMU measurements in a tightly coupled approach. The experimental results show that the performance of the proposed navigation system is better than the point-only VINS and the vision-only navigation system with points and lines.
Highlights
Indoor navigation technique, which has been widely applied in the field of mobile robot or unmanned aerial vehicle (UAV) system [1, 2], has received considerable attention in the past few years
We assume our system is tailored for any structured indoor environment where the geometric scale information can be calculated by the stereo camera and the object textures can be characterized by either point or line features
We compare the proposed navigation system (PL-visual-inertial navigation system (VINS)), with the StVO-PL [26] and the system based on multistate constraint Kalman filter (MSCKF) [15]
Summary
Indoor navigation technique, which has been widely applied in the field of mobile robot (e.g., home service robot) or unmanned aerial vehicle (UAV) system [1, 2], has received considerable attention in the past few years. For conventional feature-based visual navigation system, feature extraction methods are applied to extract the point features from images collected by the camera. We choose the stereo camera to acquire images of indoor environments; thereafter, points and lines features are all extracted from these images. Inertial sensors usually contain three orthogonal gyroscopes and accelerometers to measure the angular velocity and acceleration of the carrier and estimate the carrier’s motion in high frequency. We present a visual-inertial navigation system with point and line features for indoor environments.
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