SISTEM PENAPISAN DERAU PADA SENSOR INERSIA WAHANA TANPA AWAK QUADROTOR

Dalam penelitian ini dirancang algoritme penapisan yang bertujuan untuk mengurangi derau yang ada pada hasil pengukuran sensor inersia quadrotor. Kalman filter digunakan untuk menapis derau yang tercampur pada data hasil pengukuran sensor accelerometer dan gyroscope. Selain itu, algoritme zero velocity compensator dirancang untuk menghilangkan pergeseran ketika quadrotor berada dalam keadaan statis. Berdasarkan pengujian yang telah dilakukan algoritme zero velocity compensator yang dirancang telah mampu mengurangi pergeseran (drift) pada saat quadrotor dalam keadaaan diam, selain itu Kalman filter yang digunakan pada sensor accelerometer dan sensor gyroscope telah dapat mengurangi derau yang tercampur pada raw data, sehingga hasil integrasi perpindahan lebih baik dibandingkan dengan hasil integrasi tanpa penapisan. Kata kunci: kalman filter, zero velocity compensator, IMU

Real-time smart and standalone vision/IMU navigation sensor

In this paper, we present a smart, standalone, multi-platform stereo vision/IMU-based navigation system, providing ego-motion estimation. The real-time visual odometry algorithm is run on a nano ITX single-board computer (SBC) of 1.9 GHz CPU and 16-core GPU. High-resolution stereo images of 1.2 megapixel provide high-quality data. Tracking of up to 750 features is made possible at 5 fps thanks to a minimal, but efficient, features detection–stereo matching–feature tracking scheme runs on the GPU. Furthermore, the feature tracking algorithm benefits from assistance of a 100 Hz IMU whose accelerometer and gyroscope data provide inertial features prediction enhancing execution speed and tracking efficiency. In a space mission context, we demonstrate robustness and accuracy of the real-time generated 6-degrees-of-freedom trajectories from our visual odometry algorithm. Performance evaluations are comparable to ground truth measurements from an external motion capture system

An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times

Robust embedded egomotion estimation

This work presents a method for estimating the egomotion of an aerial vehicle in challenging industrial environments. It combines binocular visual and inertial cues in a tightly-coupled fashion and operates in real time on an embedded platform. An extended Kalman filter fuses measurements and makes motion estimation rely more on inertial data if visual feature constellation is degenerate. Errors in roll and pitch are bounded implicitly by the gravity vector. Inertial sensors are used for efficient outlier detection and enable operation in poorly and repetitively textured environments. We demonstrate robustness and accuracy in an industrial scenario as well as in general indoor environments. The former is accompanied by a detailed performance evaluation supported with ground truth measurements from an external tracking system