Christoffer R. Heckman

Toward efficient navigation in uncertain gyre-like flows

We present the development and experimental validation of an autonomous surface/underwater vehicle control strategy that leverages the environmental dynamics and uncertainty to navigate in a stochastic fluidic environment. We assume that the workspace is composed of the union of a collection of disjoint regions, each bounded by Lagrangian coherent structures (LCSs). LCSs are dynamical features in the flow field that behave like invariant manifolds in general time-invariant dynamical systems and delineate the boundaries of attraction basins. We analyze a passive particle’s noise-induced transition between adjacent LCS-bounded regions and show how most probable escape trajectories with respect to the transition probability between adjacent LCS-bounded regions can be determined. Additionally, we show how the likelihood of transition can be controlled through minimal actuation. The result is an energy efficient navigation strategy that leverages the inherent dynamics of the surrounding flow field for mobile sensors operating in a noisy fluidic environment. We experimentally validate the proposed vehicle control strategy and analyze its theoretical properties. Our results show that the single vehicle control parameter exhibits a predictable exponential scaling with respect to the escape times and is effective even in situations where the structure of the flow is not fully known and control effort is costly.

Small and Adrift with Self-Control: Using the Environment to Improve Autonomy

We present information theoretic search strategies for single and multi-robot teams to localize the source of a chemical spill in turbulent flows. In this work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies. Using the spatial distribution of the sensor readings, robots construct a belief distribution for the source location. Motion strategies are designed to maximize the change in entropy of this belief distribution. In addition, we show how a geophysical description of the environmental dynamics can improve existing motion control strategies. This is especially true when process and vehicle dynamics are intricately coupled with the environmental dynamics. We conclude with a summary of current efforts in robotic tracking of coherent structures in geophysical flows. Since coherent structures enables the prediction and estimation of the environmental dynamics, we discuss how this geophysical perspective can result in improved control strategies for autonomous systems.

Drift-correcting self-calibration for visual-inertial SLAM

We present a solution for online simultaneous localization and mapping (SLAM) self-calibration in the presence of drift in calibration parameters in order to support accurate long-term operation. Calibration parameters such as the camera focal length or camera-to-IMU extrinsics are frequently subject to drift over long periods of operation, inducing cumulative error in the reconstruction. The key contributions are modeling calibration parameters as a spatiotemporal quantity: sensor-to-sensor spatial calibration and sensor intrinsic parameters are continuously time-varying, with statistical tests for change detection and regression. An analysis of the long term effects of inappropriately modeling time-varying sensor calibration is also provided. Constant-time operation is achieved by selecting only a fixed number of informative segments of the trajectory for calibration parameter estimation, giving the added benefit of avoiding early linearization errors by not rolling past measurements into a prior distribution. Our approach is validated with simulated and real-world data.

Multi-Sensor SLAM with Online Self-Calibration and Change Detection

We present a solution for constant-time self-calibration and change detection of multiple sensor intrinsic and extrinsic calibration parameters without any prior knowledge of the initial system state or the need of a calibration target or special initialization sequence. This system is capable of continuously self-calibrating multiple sensors in an online setting, while seamlessly solving the online SLAM problem in real-time. We focus on the camera-IMU extrinsic calibration, essential for accurate long-term vision-aided inertial navigation. An initialization strategy and method for continuously estimating and detecting changes to the maximum likelihood camera-IMU transform are presented. A conditioning approach is used, avoiding problems associated with early linearization. Experimental data is presented to evaluate the proposed system and compare it with artifact-based offline calibration developed by our group.

Light Source Estimation in Synthetic Images

We evaluate a novel light source estimation algorithm with synthetic image data generated using a custom path-tracer. We model light as an environment map as light sources at infinity for its benefits in estimation. However the synthetic image data are rendered using spherical area lights as to better represent the physical world as well as challenge our algorithm. In total, we generate 55 random illumination scenarios, consisting of either one or two spherical area lights with different intensities and positioned at different distances from the observed scene. Using this data we are able to tune our optimization parameters and determine under which conditions this algorithm and model representation is best suited.