Abstract
Abstract. Vision based obstacle detection using stereo images is an essential way for hazard avoidance and path planning in planetary rover missions. However, due to light condition changes and topographic relief, only partial or sparse three-dimensional points may be derived by image matching and triangulation reconstruction, which is not sufficient for recognizing obstacles. In this paper, we developed a strategy to detect obstacles using rover stereo images by combining both image grayscale information and sparse 3D point information. Experiments were carried out using stereo images captured by navigation cameras mounted on the Yutu rover of Chang’e-3 mission. Moreover, how obstacle localization accuracy affected by the parameters are analysed and discussed.
Highlights
Planetary rover exploration is the most direct way of exploring a planet’s surface and subsurface, and because rover safety is a priority, obstacle detection is an essential task in support of rover activities, such as target localization, hazard avoidance, terrain traversability estimation and path planning (Ghosh and Biswas,2017)
Optical camera based obstacle detection methods can be divided into three types according to the dimensionality of the information used for object detection: (1) two-dimensional (2D) image information acquired by a monocular camera; (2) threedimensional (3D) information obtained by stereo images; and (3) a combination of both 2D and 3D information
We proposed strategies for obstacle detection from planetary images under unfavourable imaging conditions
Summary
Planetary rover exploration is the most direct way of exploring a planet’s surface and subsurface, and because rover safety is a priority, obstacle detection is an essential task in support of rover activities, such as target localization, hazard avoidance, terrain traversability estimation and path planning (Ghosh and Biswas,2017). Castano et al (2005) used multiple denoising methods and bilateral filtering to remove false textures, applied Sobel or Canny operators to detect edges and joined them to obtain closed contours. This procedure was implemented on a multi-scale pyramid image to detect several scales of stones. As an alternative machine-learning-based method, Thompson and Castano (2007) investigated a pixel-wise classification algorithm using support vector machine based on the local intensity values of each pixel in the image These results yielded individual points that were mostly on the rocks and not the contours
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