Characterization of objects in all dimensions at a microscopic level is important in numerous applications including surface analysis on planetary bodies. Existing microscopes fit for this task are large bench-top devices unsuitable for in-situ use, particularly in resource-constrained remote robotic exploration. Computational imaging techniques present a powerful means to overcome physical limitations in fielded sensors, but have seen especially little use in space applications. We present a miniature (150 gram) 3D microscopic imager without moving parts capable of providing 1-megapixel images at approximately 1 micron horizontal and 5 micron vertical resolution. This device combines light-field imaging and photometric stereo to provide both 3D reconstruction and reflectance characterization of individual soil grains. We thoroughly evaluate its performance by designing and nanofabricating a 3D-fiducial and further demonstrate its operation on a library of planetary soil simulants. This system opens vast opportunities for extension, demonstrating the potential of computational imaging to amplify sensing capabilities in space.

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.

The virtual bumper is a safeguarding mechanism for autonomous and remotely operated robots to conservatively avoid collisions even in the face of sensor uncertainty. In this paper we take a new approach to the virtual bumper system by applying a powerful but rarely examined statistical test. By using a modified version of Wald's sequential probability ratio test, we demonstrate that we can reduce the number of false positives reported by the virtual bumper, thereby saving valuable mission time. We further use the concept of sequential probability ratio to control vehicle speed in the presence of possible obstacles in order to increase certainty about whether or not obstacles are present. With this principled obstacle certainty measure, our new algorithm reduces the chances of collision by approximately 98% relative to traditional virtual bumper safeguarding without speed control.

This paper compares a broad cross-section of range sensing technologies for underground void modeling. In this family of applications, a tunnel environment is incrementally mapped with range sensors from a mobile robot to recover scene geometry. Distinguishing contributions of this work include an unprecedented number of configurations evaluated utilizing common methodology and metrics as well as a significant in situ environmental component lacking in prior characterization work. Sensors are experimentally compared against both an ideal geometric target and in example void environments such as a mine and underground tunnel. Three natural groupings of sensors were identified from these results and performances were found to be strongly cost-correlated. While the results presented are specific to the experimental configurations tested, the generality of tunnel environments and the metrics of reconstruction are extensible to a spectrum of outdoor and surface applications.