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

By Adam Bry, Abraham Bachrach, Nicholas A. Roy. 2012

183 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Artificial Intelligence

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Citations in the corpus

Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, background): “Additionally, state-of-the-art state estimation pipelines may require expensive sensors [2], have high computational costs [22], or be subject to drift due to the use of compressed maps [18].”
Learning monocular reactive UAV control in cluttered natural environments (2012, methodology): “Bachrach et al. [2] demonstrated using scanning lidars for Simultaneous Localization and Mapping (SLAM) in unknown indoor environments, and Bry et al. [3] showed how to use the same sensor for fast flight in indoor environments.”
Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV (2019, unknown): “With the ability to provide long range, highly accurate 3D measurements of the surrounding environment, light detection and ranging (LiDARs) is becoming an essential sensor in many robotic applications, such as autonomous driving vehicles [1], drones [2, 3], surveying, and mapping [4, 5].”
Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness (2017, unknown): “[5] combine an inertial measurement unit (IMU) with a laser range finder to localize a fixed-wing MAV, enabling reliable flight in”
Autonomous aerial navigation using monocular visual‐inertial fusion (2018, methodology): “We use an error-state representation to linearize the nonlinear system (11) and solve it via the Gauss-Newton algorithm.”
Pushbroom stereo for high-speed navigation in cluttered environments (2014, methodology): “A second ODROID-U2, communicating over LCM [18], runs our state-estimatator (a 12-state Kalman filter from [7]) and connects to our low-level interface, a firmware-modified APM 2.”
Deep Neural Network for Real-Time Autonomous Indoor Navigation (2015, methodology): “Bry et al. [11] presented a state estimation method using an on-board laser range ﬁnder and inertial measurement unit and showed aggressive ﬂight in GPS-denied environments.”
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot (2015, methodology): “Following the linearization derivations from [14], Euler integration is used to produce the a priori state and covariance.”
R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package (2021, methodology): “INTRODUCTION Recently, LiDAR sensors have been increasingly used in various robotic applications such as autonomous driving vehicles [1], drones [2]–[4], etc.”
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments (2016, background): “Simultaneously, advances in fast, accurate state estimation methods have enabled these vehicles to fly through dense, cluttered spaces without the need for a motion capture system [4, 25].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, background): “Pushing control, state estimation and localization to the limits, Bry et al. (2012) have shown very impressive maneuvers based on known maps in confined indoor spaces using a fixed wing aircraft equipped with a laser-range finder, while having a stall speed of 10m/s. Similar aggressive maneuvers…”
Bayesian Learning for Safe High-Speed Navigation in Unknown Environments (2015, methodology): “We use the LIDAR to provide relative pose updates to the EKF using a scan-matching algorithm [4].”