Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.

Abstract-We describe CHISEL: a system for real-time housescale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field [2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets [3], and on datasets collected in real-time from two devices.

Robot 3D (three-dimension) path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints (including geometric, physical, and temporal constraints). The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

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An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques.