Abraham Bachrach

Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera

RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of their 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 state-of-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. We evaluate the effectiveness of our system for stabilizing and controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.

RANGE–Robust autonomous navigation in GPS-denied environments

This paper addresses the problem of autonomous navigation of a micro air vehicle (MAV) in GPS-denied environments. We present experimental validation and analysis for our system that enables a quadrotor helicopter, equipped with a laser range finder sensor, to autonomously explore and map unstructured and unknown environments. The key challenge for enabling GPS-denied flight of a MAV is that the system must be able to estimate its position and velocity by sensing unknown environmental structure with sufficient accuracy and low enough latency to stably control the vehicle. Our solution overcomes this challenge in the face of MAV payload limitations imposed on sensing, computational, and communication resources. We first analyze the requirements to achieve fully autonomous quadrotor helicopter flight in GPS-denied areas, highlighting the differences between ground and air robots that make it difficult to use algorithms developed for ground robots. We report on experiments that validate our solutions to key challenges, namely a multilevel sensing and control hierarchy that incorporates a high-speed laser scan-matching algorithm, data fusion filter, high-level simultaneous localization and mapping, and a goal-directed exploration module. These experiments illustrate the quadrotor helicopters ability to accurately and autonomously navigate in a number of large-scale unknown environments, both indoors and in the urban canyon. The system was further validated in the field by our winning entry in the 2009 International Aerial Robotics Competition, which required the quadrotor to autonomously enter a hazardous unknown environment through a window, explore the indoor structure without GPS, and search for a visual target.

Autonomous Flight in Unknown Indoor Environments

This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner.1 Finally, we show experimental results demonstrating the helicopters ability to navigate accurately and autonomously in unknown environments.

Multiple relative pose graphs for robust cooperative mapping

This paper describes a new algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate online multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs that avoids the initialization problem and leads to an efficient solution when compared to a completely global formulation. The relative pose graphs are optimized together to provide a globally consistent multi-robot solution. Efficient access to covariances at any time for relative parameters is provided through iSAM, facilitating data association and loop closing. The performance of the technique is illustrated on various data sets including a publicly available multi-robot data set. Further evaluation is performed in a collaborative helicopter and ground robot experiment.

State estimation for aggressive flight in GPS-denied environments using onboard sensing

In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment.

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

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 state-of-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 an unreliable wireless link to a ground station. 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 algorithm prentice2009belief, 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.

Increasing autonomy of UAVs

This article has presented a tightly integrated systems architecture for a decentralized CSAT mission management algorithm and demonstrated successful implementation in actual hardware flight tests. This CSAT architecture allows each UAV to accomplish a combined search and track mission by conceptualizing the searching aspect as a spare time strategy to be executed optimally over a short time horizon when the agents are not actively tracking a vehicle. This presented a balance between the two conflicting search and track modes and allowed the mission to achieve more than simply searching or tracking alone.

On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

The MAV ’08 competition focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. To execute this mission, a number of technical challenges were addressed, including designing the micro air vehicle (MAV), using the MAV to geo-locate ground targets, and planning the motion of ground vehicles to reach the hostage location without detection. In this paper, we describe the complete system designed for the MAV ’08 competition, and present our solutions to three technical challenges that were addressed within this system. First, we summarize the design of our MAV, focusing on the navigation and sensing payload. Second, we describe the vision and state estimation algorithms used to track ground features, including stationary obstacles and moving adversaries, from a sequence of images collected by the MAV. Third, we describe the planning algorithm used to generate motion plans for the ground vehicles to approach the hostage building undetected by adversaries; these adversaries are tracked by the MAV from the air. We examine different variants of a search algorithm and describe their performance under different conditions. Finally, we provide results of our system’s performance during the mission execution.

RANGE - robust autonomous navigation in GPS-denied environments

This video highlights our system that enables a Micro Aerial Vehicle (MAV) to autonomously explore and map unstructured and unknown GPS-denied environments. While mapping and exploration solutions are now well-established for ground vehicles, air vehicles face unique challenges which have hindered the development of similar capabilities. Although there has been recent progress toward sensing, control, and navigation techniques for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real-world environments. Our system leverages a multi-level sensing and control hierarchy that matches the computational complexity of the component algorithms with the real-time needs of a MAV to achieve autonomy in unconstrained environments.

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

In this paper, we describe trajectory planning and state estimation algorithms for aggressive flight of micro aerial vehicles in known, obstacle-dense environments. Finding aggressive but dynamically feasible and collision-free trajectories in cluttered environments requires trajectory optimization and state estimation in the full state space of the vehicle, which is usually computationally infeasible on realistic timescales for real vehicles and sensors. We first build on previous work of van Nieuwstadt and Murray and Mellinger and Kumar, to show how a search process can be coupled with optimization in the output space of a differentially flat vehicle model to find aggressive trajectories that utilize the full maneuvering capabilities of a quadrotor. We further extend this work to vehicles with complex, Dubins-type dynamics and present a novel trajectory representation called a “Dubins–Polynomial trajectory”, which allows us to optimize trajectories for fixed-wing vehicles. To provide accurate state estimation for aggressive flight, we show how the Gaussian particle filter can be extended to allow laser rangefinder localization to be combined with a Kalman filter. This formulation allows similar estimation accuracy to particle filtering in the full vehicle state but with an order of magnitude more efficiency. We conclude with experiments demonstrating the execution of quadrotor and fixed-wing trajectories in cluttered environments. We show results of aggressive flight at speeds of up to 8 m/s for the quadrotor and 11 m/s for the fixed-wing aircraft.