Keyframe-Based Tightly-Coupled SLAM with RGBD Camera and IMU

In this paper, we propose a novel inertial-assisted slam system intended for low-cost micro aerial vehicles (MAVs). The system sensor assembly consists of color camera, depth camera and a inertial measurement unit (IMU) with three-axis accelerometers/gyroscopes. We use IMU data to enable probability-based predetermined operations rather than hypothesis testing iterations to accelerate pose calculation and obtain its optimal estimation through nonlinear optimization. We illustrate the performance of our system by hovering a MAV in a GPS-denied environment which position accuracy can reach 3.9cm RMSE in a 189 second flight and its robustness is also illustrated in a complex indoor environment. Introduction As portable computing devices, such as smart phones, smart glasses and other devices, become more ubiquitous, there is an interest to provide such devices with localization and mapping capabilities. Localization can be partially addressed by relying on systems that use global positioning system (GPS) signals or triangulation of cell tower signals to calculate position. However, such services tend to be limited to use outdoors, since GPS signals or cell tower signals may be blocked within buildings. Moreover, commercial localization and mapping services are generally unable to provide accuracy higher than several meters with respect to position. Visual-based inertial navigation systems rely on information obtained from images and inertial measuring devices in order to achieve localization and mapping. Since visual-based inertial navigation systems do not require signals from GPS or cell towers, such systems may be used indoors where GPS and cell signals cannot reach or are unavailable due to obstacle or interference. Furthermore, visual-based inertial navigation systems enable very high position accuracy, e.g., on the order of centimeters. However, visual-based inertial navigation systems are typically computationally intensive as they need to process large amounts of image data acquired from an image detector, such as a camera, and inertial measurement unit (IMU), all in real-time. In addition, to achieve highly accurate measurements of position, a history of information related to previous poses (positions and orientations), inertial measurements and image features is typically stored, thus requiring device to use a substantial amount of memory and consequently large computation time to process this information. System overview. The visual-inertial fusion approaches found in the literature can be categorized to follow two approaches. In loosely-coupled systems, e.g. [1], the IMU measurements are incorporated as independent inclinometer and relative yaw measurements into the stereo vision optimization. Weiss et al. [2] use vision-only pose estimates as updates to an EKF with indirect IMU propagation. Also in [3], relative stereo pose estimates are integrated into a factor-graph containing inertial terms and absolute GPS measurements. Such methods limit the complexity, but disregard correlations amongst internal states of different sensors. 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 71