Jizhong Xiao

Fast visual odometry and mapping from RGB-D data

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.

Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles

Advancing research into autonomous micro aerial vehicle navigation requires data structures capable of representing indoor and outdoor 3D environments. The vehicle must be able to update the map structure in real time using readings from range-finding sensors when mapping unknown areas; it must also be able to look up occupancy information from the map for the purposes of localization and path-planning. Mapping models that have been used for these tasks include voxel grids, multi-level surface maps, and octrees. In this paper, we suggest a new approach to 3D mapping using a multi-volume occupancy grid, or MVOG. MVOGs explicitly store information about both obstacles and free space. This allows us to correct previous potentially erroneous sensor readings by incrementally fusing in new positive or negative sensor information. In turn, this enables extracting more reliable probabilistic information about the occupancy of 3D space. MVOGs outperform existing probabilistic 3D mapping methods in terms of memory usage, due to the fact that observations are grouped together into continuous vertical volumes to save space. We describe the techniques required for mapping using MVOGs, and analyze their performance using indoor and outdoor experimental data.

Modular wall climbing robots with transition capability

This paper introduces two wall climbing robots based on different adhesive mechanisms: vortex attraction technique and the vacuum rotor package. The robots adopt modular design with each module can move on various smooth/rough surfaces independently while a combination of two modules can achieve wall-to-wall transition. Detailed description of the novel mechanical and electrical design is presented. Simulation is conducted to reveal aerodynamic behavior of the adhesive mechanism. Several robot prototypes are built to verify the design concepts. Future directions to improve the climbing robots are elaborated

Backstepping based multiple mobile robots formation control

In this paper, we investigate the leader following based formation control of multiple nonholonomic mobile robots. We present a new kinematics model for the leader-follower system using Cartesian coordinates rather than the commonly used polar coordinates in literature. Based on this new model and the idea of integrator backstepping, a globally stable controller is derived for the whole system. Simulation results are included to verify the efficacy of the presented new model and controller.

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

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.

Modeling multiple robot systems for area coverage and cooperation

This paper presents a distributed model for cooperative multiple mobile robot systems. In a multiple robot system, each mobile robot has sensing, computation and communication capabilities. The mobile robots spread out across certain area and share sensory information through an ad hoc wireless network. The multiple mobile robot system is therefore a mobile sensor network. In this paper, Voronoi diagram and Delaunay triangulation are introduced to model the area coverage and cooperation of mobile sensor networks. Based on the model, this paper discusses a fault tolerant algorithm for autonomous deployment of the mobile robots. The algorithm enables the system to reconfigure itself such that the area covered by the system can be enlarged. The proposed formation control algorithm allows the mobile sensor network to track moving target and sweep a larger area along specified paths.

Autonomous quadrotor flight using onboard RGB-D visual odometry

In this paper we present a navigation system for Micro Aerial Vehicles (MAV) based on information provided by a visual odometry algorithm processing data from an RGB-D camera. The visual odometry algorithm uses an uncertainty analysis of the depth information to align newly observed features against a global sparse model of previously detected 3D features. The visual odometry provides updates at roughly 30 Hz that is fused at 1 KHz with the inertial sensor data through a Kalman Filter. The high-rate pose estimation is used as feedback for the controller, enabling autonomous flight. We developed a 4DOF path planner and implemented a real-time 3D SLAM where all the system runs on-board. The experimental results and live video demonstrates the autonomous flight and 3D SLAM capabilities of the quadrotor with our system.

City-Climber: A New Generation Wall-Climbing Robots

An increasing interest in the development of special climbing robots has been witnessed in last decade. Motivations are typically to increase the operation efficiency in dangerous environments or difficult-to-access places, and to protect human health and safety in hazardous tasks. Climbing robots with the ability to maneuver on vertical surfaces are currently being strongly requested by various industries and military authorities in order to perform dangerous operations such as inspection of high-rise buildings, spray painting and sand blasting of gas tanks, maintenance of nuclear facilities, aircraft inspection, surveillance and reconnaissance, assistance in fire fighting and rescue operations, etc. Such capabilities of climbing robots would not only allow them to replace human workers in those dangerous duties but also eliminate costly scaffolding.

A Linear Kalman Filter for MARG Orientation Estimation Using the Algebraic Quaternion Algorithm

Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm.

An open-source pose estimation system for micro-air vehicles

This paper presents the implementation of an open-source 6-DoF pose estimation system for micro-air vehicles and considers the future implications and benefits of open-source robotics. The system is designed to provide high frequency pose estimates in unknown, GPS-denied indoor environments. It requires a minimal set of sensors including a planar laser range-finder and an IMU sensor. The code is optimized to run entirely onboard, so no wireless link and ground station are explicitly needed. A major focus in our work is modularity, allowing each component to be benchmarked individually, or swapped out for a different implementation, without change to the rest of the system. We demonstrate how the pose estimation can be used for 2D SLAM or 3D mapping experiments. All the software and hardware which we have developed, as well as extensive documentation and test data, is available online.