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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 1cm voxel dense maps near real-time. VIL-SLAM demonstrates improved accuracy and robustness compared to state-of-the-art LiDAR methods., Comment: Submitted to RA-L with IROS 2019 option

A next challenge in planetary exploration involves probing the subsurface to understand composition, to search for volatiles like water ice, or to seek evidence of life. The Mars rover missions have scraped the surface of Mars and cored rocks to make ground breaking discoveries. Many believe that the chance of finding evidence of life is expected to increase by going deeper. Deploying a system that probes the subsurface brings its own challenges and to that end, we designed, built and field tested an autonomous robot that can collect subsurface samples using a 1 m drill. The drill operation, sample transfer, and sample analysis are all automated. The robot also navigates kilometers autonomously while making decisions about scientific measurements. The system is designed to execute multi-day science plans, stopping and resuming operation as necessary. This paper describes the robot and science instruments and lessons from designing and operating such a system.

Plant phenotyping is a time consuming, labour intensive, error prone process of measuring the physical properties of plants. We present a scalable robotic system which employs computer vision and machine learning to phenotype plants rapidly. It maintains high throughput making multiple phenotyping measurements during the plant lifecycle in plots containing thousands of plants. Our novel approach allows scanning of plants inside the plant canopy in addition to the top and bottom section of the plants. Here we present our design decisions, implementation challenges and field observations.

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.