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Alexandrov, Oleg

Publications and source records attributed to Alexandrov, Oleg.

Surface Reconstruction of a Challenging Region with Permanent Shadows on the Moon

As the Earth is orbiting the Sun, there is the region a sunlight can't be reached on the Moon, a satellite of the Earth. Usually, the permanent shadows are located near a pole area and the surface reconstruction in the permanent shadows region is challenging because of the lack of sunlight.In this research, the reconstruction method containing the permanent shadows is suggested. To apply this method, the time-varying shadow region and a permanently shadowed region are needed to be separated. In the time-varying shadow region, the pixel intensity of the image can be composed of the albedo and the reflectance model. Using the light source direction from ISIS3 of USGS, the shape of the region can be extracted from the reflectance model. Though this region is visible in the camera, the change of illumination is quite large in this region, it is hard to reconstruct the shape using stereo-photogrammetry or SfS. So, it is needed of collecting more surface normal information from several pixel level-aligned images. In this process, the albedo information can be initialized and enhanced easily from many images.In the middle of permanent shadows, the high-resolution DEM is generated by LOLA DEM. The LOLA DEM is generated by interpolation between measured data point strips, the actual scene of the permanent shadows. Using the sensor like DIVINER, the Imaginary scene generation via a trained network between the sunlight sensor and other kinds of a sensor is suggested.

Moon, Sunghyun↗

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee,a new free-flying robot designed to navigate autonomously on board the International Space Station (ISS). Astrobee will conduct experiments in microgravity, as well as assisst astronauts and ground controllers. Astrobee replaces the SPHERES robots which currently operate on the ISS, which were limited to operating in a small cube since their localization system relied on triangulation from ultrasonic transmitters. Astrobee localizes with only monocular vision and an IMU, enabling it to traverse the entire US segment of the station. Features detected on a previously-built map, optical flow information,and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise.Finally, we extensively evaluate the behavior of the filter on atwo-dimensional testing surface.

Coltin, Brian↗

Photometric Lunar Surface Reconstruction

Accurate photometric reconstruction of the Lunar surface is important in the context of upcoming NASA robotic missions to the Moon and in giving a more accurate understanding of the Lunar soil composition. This paper describes a novel approach for joint estimation of Lunar albedo, camera exposure time, and photometric parameters that utilizes an accurate Lunar-Lambertian reflectance model and previously derived Lunar topography of the area visualized during the Apollo missions. The method introduced here is used in creating the largest Lunar albedo map (16% of the Lunar surface) at the resolution of 10 meters/pixel.

Albedo reconstruction↗