Spectral mapping, typically performed at the orbital scale, is hard at the rover scale as it necessitates larger coverage and field operable spectrometers. In this work, we propose using RGB cameras to assist spectral mapping. RGB cameras placed on a wide range of robotic platforms, including aerial vehicles, which can explore large regions compared to ground rovers. Our method uses a spectral model which learns the spatial relationship of spectra in a compressed feature space. We show that RGB data can contribute to this feature space and thereby enhance spectral reconstruction accuracy.

Perception using stereo requires external light. In the absence of natural light, active, structured light provides light where it is needed. In this work, we demonstrate how a free moving line striping laser can be used to perceive and model terrains. In this formulation, we do not need to know the position of the laser with respect to the stereo pair which precludes the need for calibrating the laser position. This also saves an actuator as the laser can be passively articulated using a spring. In this paper, we present the algorithms to efficiently extract the laser pixels from the stereo image pair. We also present techniques to use the structure of the light and overcome degenerate cases to build cleaner maps. This perception method benefits micro-rovers specifically those trying to operate in the extreme lighting conditions.

Maintaining high levels of productivity for planetary rover missions is very difficult due to limited communication and heavy reliance on ground control. There is a need for autonomy that enables more adaptive and efficient actions based on real-time information. This paper presents an autonomous mapping and exploration approach for planetary rovers. We first describe a machine learning model that actively combines remote and rover measurements for mapping. We focus on spectroscopic data because they are commonly used to investigate surface composition. We then incorporate notions from information theory and non-myopic path planning to improve exploration productivity. Finally, we demonstrate the feasibility and successful performance of our approach via spectroscopic investigations of Cuprite, Nevada; a well-studied region of mineralogical and geological interest. We first perform a detailed analysis in simulations, and then validate those results with an actual rover in the field in Nevada.

Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VIL-SLAM accomplishes this by incorporating tightly-coupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loop-closure corrected 6-DOF LiDAR poses in real-time and lcm voxel dense maps near real-time. VIL-SLAM demonstrates improved accuracy and robustness compared to state-of-the-art LiDAR methods.

Automatically correlating plant observable characteristics to their underlying genetics will streamline selection methods in plant breeding. Measurement of plant observable characteristics is called phenotyping, and knowing plant phenotypes accurately and throughout a plant's growth is central to making breeding decisions. In-field plant phenotyping in an automated and noninvasive manner is hence crucial to accelerating plant breeding methods. However, most of the existing methods on plant phenotyping using visual imaging are confined to controlled greenhouse environments. This paper presents an automated method of mapping 2D images collected in an outdoor sorghum field to segmented 3D plant units that are of interest for phenotyping. This method leverages multiple horizontal and vertical viewpoints while capturing 2D images from a robotic platform so as to generate in-field 3D reconstructions of the sorghum plant. We develop and quantitatively evaluate segmentation methods on these 3D reconstructions and also compare against reconstructions obtained from a controlled greenhouse environment. We present analysis that contrasts the role of purely local geometric features and the effect of addition of global context in both datasets. This work furthers capabilities of in-field phenotyping which paves the way forward for plant biologists to study the coupled effect of genetics and environment on improving crop yields.