Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using unmanned aerial vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper, we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

This paper describes a structured light-based sensor for hazard avoidance in planetary environments. The system presented here can also be used in terrestrial applications constrained by reduced onboard power and computational complexity and low illumination conditions. The sensor consists on a calibrated camera and laser dot projector system. The onboard hazard avoidance system determines the position of the projected dots in the image and through a triangulation process detects potential hazards. The paper presents the design parameters for this sensor and describes the image based solution for hazard avoidance. The system presented here was tested extensively in day and night conditions in Lunar analogue environments. The current system achieves over 97% detection rate with 1.7% false alarms over 2000 images.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas it will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAV) to cover wildfire is promising because it can replace humans for fire tracking, reducing hazards and saving operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

Planetary rovers navigate in extreme environments for which a Global Positioning System (GPS) is unavailable, maps are restricted to relatively low resolution provided by orbital imagery, and compass information is often lacking due to weak or not existent magnetic fields. However, an accurate rover localization is particularly important to achieve the mission success by reaching the science targets, avoiding negative obstacles visible only in orbital maps, and maintaining good communication connections with ground. This paper describes a horizon solution for precise rover orientation estimation. The detected horizon in imagery provided by the on board navigation cameras is matched with the horizon rendered over the existing terrain model. The set of rotation parameters (roll, pitch yaw) that minimize the cost function between the two horizon curves corresponds to the rover estimated pose.

This paper introduces an advanced rover localization system suitable for autonomous planetary exploration in the absence of Global Positioning System (GPS) infrastructure. Given an existing terrain map (image and elevation) obtained from satellite imagery and the images provided by the rover stereo camera system, the proposed method determines the best rover location through visual odometry, 3D terrain and horizon matching. The system is tested on data retrieved from a 3 km traverse of the Basalt Hills quarry in California where the GPS track is used as ground truth. Experimental results show the system presented here reduces by over 60% the localization error obtained by wheel odometry.

Planetary missions generate a large quantity of image data. In flight operations or on servers such as Planetary Data Systems (PDS) these data products are only searchable by keys such as the Sol, spacecraft clock, or rover motion counter index, with little connection to the semantic content of the images. During mission science operations the science team typically pores over each and every data product returned, which may not require more sophisticated organization and search tools. However, for analyzing existing image databases with thousands or millions of images, manual searching, matching, or classification is intractable. In this paper, we present a method for matching images based on similarities in visual texture. For every image in a database, a series of filters are used to compute the response to localized frequencies and orientations. Filter responses are turned into a low dimensional descriptor vector, generating a 37 dimensional fingerprint. At query time, fingerprints are quickly matched to find images with similar appearance. Image databases containing several thousand images are pre-processed offline in a matter of hours. Image matches from the database are found in a matter of seconds. We have demonstrated this image matching technique using three sources of data and carried out user tests to evaluate matching performance by hand labeling results. User tests verify approximately 40 % false positive rate within the top 14 matches. This represents a powerful search tool for databases of thousands of images where the a priori match probability for an image might be less than 1%.

This paper presents an efficient and robust method for registration of terrain models created using stereovision on a planetary rover. Our approach projects two surface models into a virtual depth map, rendering the models, as they would be seen from a single range sensor. Correspondence is established based on which points project to the same location in the virtual range sensor. A robust norm of the deviations in observed depth is used as the objective function, and the algorithm searches for the rigid transformation, which minimizes the norm. An initial coarse search is done using rover pose information from odometry and orientation sensing. A fine search is done using Levenberg-Marquardt. Our method enables a planetary rover to keep track of designated science targets as it moves, and to hand off targets from one set of stereo cameras to another. These capabilities are essential for the rover to autonomously approach a science target and place an instrument in contact in a single command cycle.

This paper presents an algorithm for simultaneous localization and mapping for a mobile robot using monocular vision and odometry. The approach uses variable state dimension filtering (VSDF) framework to combine aspects of extended Kalman filtering (EKF) and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use the maximally informative statistics criterion to derive an interpolation scheme for linearization in recursive filtering. The interpolation is based on fitting a set of deterministic samples rather than using analytic Jacobians. The second improvement is to replace the inverse covariance matrix-in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the localization and mapping are presented.

This paper presents an algorithm for simultaneous localization and map building for a mobile robot moving in an unknown environment. The robot can measure only the bearings to identifiable targets and its own relative motion. The approach is to recursively estimate features of the environment which are invariant to the robot pose in order to decouple the pose error from the map error. The highly nonlinear nature of this problem requires more explicit reasoning about the spatial relationships between landmarks and between the robot and landmarks than those used in previous methods.

Distributed detection of weak random signals in additive, possibly non-Gaussian, noise is considered for cases with multibit sensor decisions. Signal-to-noise ratios are assumed unknown and the signals at the different sensors may be statistically dependent. Analytical expressions are provided that describe the best way to fuse the quantized observations for cases with any given number of sensors. The best schemes for originally quantizing the observations at each sensor are also studied for the case of an asymptotically large number of sensors. These schemes are shown to minimize the mean-squared error between the best weak-signal test statistic based on unquantized observations and the best weak-signal test statistic based on quantized observations. Analytical expressions describing optimum sensor quantizers are provided. The approach used to obtain these expressions insures these sensor quantizers give good performance for cases with a finite number of sensors. A novel iterative technique to search for optimum sensor quantizers efficiently is described. Numerical solutions are presented, some of which involve cases where the best schemes for independent signal observations are shown to be suboptimum.