Persistent Visual Tracking and Accurate Geo-Location of Moving Ground Targets by Small Air Vehicles

Transcription

1 Persistent Visual Tracking and Accurate Geo-Location of Moving Ground Targets by Small Air Vehicles Michael Dille, Ben Grocholsky, Stephen Thomas Nuske EXTENDED ABSTRACT 1 Motivation, Problem Statement, and Related Work Geo-location of a ground object or target of interest from live video is a common task required of small and micro unmanned aerial vehicles (SUAVs and MAVs) in surveillance and rescue applications. However, such vehicles commonly carry low-cost and light-weight sensors providing poor bandwidth and accuracy, along with slowly-reacting high-level autopilots presenting few control primitives such as waypoints and orbit points. We present an architecture encompassing estimation and control strategies that provides persistent tracking and accurate geolocation of moving targets despite such limitations in SUAV systems. Mapping a pixel in an SUAV video frame to a point in world coordinates is well studied [Conte et al., 28, Redding et al., 26, Gibbins et al., 24, Madison et al., 28], however owing to the many inaccuracies in vehicle state, calibration, and terrain models, these observations may be highly erroneous. Intuitive approaches for calibration and filtering are typically applied [Barber et al., 26, Ross et al., 28], unfortunately, as the authors previously demonstrated [Nuske et al., 21] and depicted in Figure 1, sensor biases may in fact be time-varying, observation functions may be highly nonlinear, and resulting observation uncertainties can be far from Gaussian, necessitating methods that specifically represent physical sensor error sources. Moving targets are especially challenging given both the limited applicability of such filters and the need for control strategies that maintain persistent view of a target, without which estimation accuracy will falter, directing the UAV to progressively worse locations from which to view the target, quickly losing it completely. Existing examples include pursuit by orbiting [Rafi et al., 26] and optimization of information-theoretic objectives [Casbeer et al., 26], however such strategies generally fail to take into account the effects of view-constrained sensors such as cameras and the probability of sensor footprint location given vehicle pose uncertainty. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. ( mdille3@ri.cmu.edu) 1

2 2 Approach Rather than approximating inevitable sensor and world model errors as corruptive disturbances, we embrace residual post-calibration uncertainties. This paper first presents a comparative study of uncertainty representations applied to target geolocation, considering both a particle filter that well approximates the sort of true distribution shown in Figure 1(a) as well an overparameterized filter able to represent such distributions parametrically. Where available, prior information is incorporated, such as by projecting uncertainties onto road segments when the target is believed to lie on an existing road network. Given a current estimate of a moving target s state, constant action by the UAV is required to maintain view of the target and, further, to stay in a relative location providing the most useful observations. To accomplish this, we next consider several variations of receding horizon planning that evaluate the resulting observation uncertainty from predicted future UAV and target locations to maximize target viewing duration and minimize expected filter uncertainty. 3 Experimental Results, Insights, and Conclusions We present results from ongoing experiments conducted using a commercial hand-launched SUAV in over ten hours of field trials in which moving ground targets are tracked and pursued in real-time. We compare the effectiveness of baseline attempts at calibration versus the efficacy of target location estimation using each of several uncertainty representations. A brief example of such a comparison is given in Figure 2. Lastly, we provide live and simulated results from executing control strategies that inherently produce intelligent pursuit behaviors, as shown for instance in Figure 3. Ongoing analysis considers fundamental limitations of single-vehicle pursuit and the potentially immense benefits of joint planning with a second or additional UAVs. Overall, the architecture we present provides hope that through proper uncertainty modeling and information-theoretic control, the effectiveness of existing SUAV platforms may be greatly enhanced. 2

3 References [Barber et al., 26] Barber, D. B., Redding, J. D., Mclain, T. W., Beard, R. W., and Taylor, C. N. (26). Vision-based target geo-location using a fixed-wing miniature air vehicle. J. Intell. Robotics Syst., 47(4): [Casbeer et al., 26] Casbeer, D., Zhan, P., and Swindlehurst, A. (26). A non-search optimal control solution for a team of MUAVs in a reconnaissance mission. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [Conte et al., 28] Conte, G., Hempel, M., Rudol, P., Lundström, D., Duranti, S., Wzorek, M., and Doherty, P. (28). High accuracy ground target geo-location using autonomous micro aerial vehicle platforms. In AIAA Conference on Guidance, Navigation, and Control. [Gibbins et al., 24] Gibbins, D., Roberts, P., and Swierkowski, L. (24). A video geolocation and image enhancement tool for small unmanned air vehicles (UAVs). In Intelligent Sensors, Sensor Networks and Information Processing Conference, pages [Madison et al., 28] Madison, R., DeBitetto, P., Rocco Olean, A., and Peebles, M. (28). Target geolocation from a small unmanned aircraft system. In IEEE Aerospace Conference, pages [Nuske et al., 21] Nuske, S., Dille, M., Grocholsky, B., and Singh, S. (21). Representing substantial heading uncertainty for accurate target geolocation by small UAVs. In AIAA Conference on Guidance, Navigation, and Control. [Rafi et al., 26] Rafi, F., Khan, S. M., Shafiq, K. H., and Shah, M. (26). Autonomous target following by unmanned aerial vehicles. In SPIE Defense and Security Symposium, Orlando, USA. [Redding et al., 26] Redding, J., Mclain, T. W., Beard, R. W., and Taylor, C. (26). Vision-based target localization from a fixed-wing miniature air vehicle. In American Control Conference. [Ross et al., 28] Ross, J. A., Geiger, B. R., Sinsley, G. L., Horn, J. F., Long, L. N., and Niessner, A. F. (28). Vision-based target geolocation and optimal surveillance on an unmanned aerial vehicle. In AIAA Conference on Guidance, Navigation, and Control. 3

4 2 North (m) True Observation Error Linearized Gaussian Error Gaussian with true mean & std. dev. Target Observation UAV Position Bearing error (deg) Frequency Bearing error (deg) East (m) (a) Time (s) (b) Figure 1: Plots illustrating the complexity of target geolocation. (a) shows that significant heading error induces a crescent shape in a single-observation uncertainty distribution that is poorly approximated by a Gaussian linearization of the observation function, while (b) shows actual error in bearing to a repeatedly orbited target that is time-varying and non-gaussian, implying the inapplicability of fixed offsets and additive Gaussian error models. Particle Filter Linearized Kalman Filter 1 error 3 trace(p) 1 error 3 trace(p) Euclidean error (m) Euclidean error (m) Observation Index Observation Index Figure 2: Comparison of estimate error and confidence produced by the particle filter (left) and linearized Kalman filter (right) indicating that the linearized method produceds an inconsistent and overconfident result. 4

5 Figure 3: Example frames from an execution of a pursuit controller that keeps the current target estimate in the field of view and provides varied viewpoints, which are critical to reducing target estimate uncertainty. 5