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.

This video demonstrates the progress of the CityFlyer project. The goal of the project is to develop an autonomous quadrotor helicopter capable of autonomous flight in indoor and outdoor 3D environments such as hallways, stairwells, forests, caves and tunnels. Currently we are focused on developing 3D mapping and navigation capabilities for indoor environments. This video shows our 6 Degree of Freedom pose estimation system, a novel 3D map structure called Multi Volume Occupancy Grids, and the development of a groundstation.

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.

This paper presents a novel technique to register a set of two 3D laser scans obtained from a ground robot and a wall-climbing robot which operates on the ceiling to construct a complete map of the indoor environment. Traditional laser scan registration methods like the Iterative Closest Point (ICP) algorithm will not converge to a global minimum without a good initial estimate of the transformation matrix. Our technique uses an overhead camera on the wall-climbing robot to keep line of sight with the ground robot and solves the Perspective Three Point (P3P) Problem to obtain the transformation matrix between the wall-climbing robot and the ground robot, which serves as a good initial estimate for the ICP algorithm to further refine the transformation matrix. We propose a novel particle filter algorithm to identify the real pose of the wall-climbing robot out of up to four possible solutions to P3P problem using Grunert's algorithm. The initial estimate ensures convergence of the ICP algorithm to a global minimum at all times. The simulation and experimental results indicate that the resulting composite laser map is accurate. In addition, the vision-based approach increases the efficiency by reducing the number of iterations of the ICP algorithm.

This paper presents a novel method to construct a complete 3D map that includes all surfaces (ceiling, wall, and furniture tops, etc.) in indoor environments. A team of four robots, including three ground robots and one wall-climbing robot is deployed in a tetrahedron configuration that satisfies the perspective three point (P3P) problem. P3P problem is to estimate the pose of a perspective camera on the wall-climbing robot viewing three ground robots, which will produce up to four solutions using Grunert's algorithm while only one of them is genuine. We propose a probabilistic Bayesian algorithm that identifies the unique solution of the P3P problem using the mobility of the camera. Based on this technique, we introduce an intra-robot localization method to determine the geometric relationship among four robots. Each ground robot is equipped with a rotary laser range finder (LRF), a pan-tilt-zoom camera, and a LED cluster. The wall-climbing robot is fitted with a LRF, a perspective camera, and a motion sensor. Through the vision sensors, the robots obtain their relative poses by solving the P3P problem. Through the LRF on each robot, 4 laser point cloud maps are produced from each robot's point of view. With the information of relative poses of the multiple robots and the calibration data of each LRF and camera pair, the 4 partial maps are fused to acquire a complete 3D map that is rich with information of all surfaces. Our approach outperforms the traditional range image fusion algorithms in terms of time complexity and is suitable for real-time implementation. Real experiments verified the effectiveness of the method.

The video presents recent progress of the wall-climbing robot project at the City College of New York. The robots are named as City-Climbers which adopt a novel adhesive mechanism based on aerodynamic attraction to achieve good balance between strong adhesion force and high mobility. The video demonstrates that the City-Climber robots can operate on virtually any kind of smooth or rough surfaces and have the capabilities to move on the ground, climb walls, and transit between them. The modular design achieves both fast motion of each module on planar surfaces and smooth transition between the surfaces by a set of two modules. The video also displays the Fluent simulation results of the aerodynamic attraction with the aim to optimize the design. DSP-based control system is introduced which enables the robot to operate both manually and autonomously