Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments

Abstract

RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent stateof-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on unreliable wireless links. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the Belief Roadmap (BRM) algorithm (Prentice and Roy, 2009), a belief space extension of the Probabilistic Roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.Abraham Bachrach and Samuel Prentice contributed equally to this work.

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

“…Research groups on robotics have proposed different techniques for obstacle avoidance, based on sensors like LiDAR [4][5][6] and RGB-D [7,8], which show robustness to identifying obstacles; but implementing these devices in a compact MAV is difficult, expensive, and also, these consume additional electrical power. When we work with UAVs and want to implement other device onboard, we need to consider the payload that they can carry, limiting the use of any UAV.…”

“…Based on the low-level control system of Bebop, the motion in the y-axis can be controlled by the roll [48,49]. It is necessary to estimate the mathematical model that relates roll control with the linear speed in the y-axis; the transfer function of the mathematical model is shown in Equation (8). This model is simulated and presented in Figure 8 in order to observe the step response.…”

Infrastructure Mapping in Well-Structured Environments Using MAV

“…This platform was used mostly to perform autonomous exploration and navigation tasks such as the work from Shen et al [22,23], Weiss et al [24], Bachrach et al [4,6,5] and Pravitra et al [21]. The platform offers high performance on-board processors with around 600-650 grams payload.…”

Generation and weighting of 3D point correspondences for improved registration of RGB-D data

“…Du et al (2011) followed a similar approach but allowed user interaction. The RGB-D SLAM method (Engelhard et al, 2011;Endres et al, 2012) and the method of Bachrach et al, (2012) were both based on the idea of initial registration using visual feature points, although they used different feature extraction operators. Dryanovski et al (2012) performed the initial registration based on edge features extracted from the colour images.…”