Planetary Monocular Simultaneous Localization and Mapping

Abstract

Planetary monocular simultaneous localization and mapping (PM-SLAM), a modular, monocular SLAM system for use in planetary exploration, is presented. The approach incorporates a biologically inspired visual saliency model (i.e., semantic feature detection) for visual perception in order to improve robustness in the challenging operating environment of planetary exploration. A novel method of generating hybrid-salient features, using point-based descriptors to track the products of the visual saliency models, is introduced. The tracked features are used for rover and map state-estimation using a SLAM filter, resulting in a system suitable for use in long-distance autonomous (micro)rover navigation, and the inherent hardware constraints of planetary rovers. Monocular images are used as an input to the system, as a major motivation is to reduce system complexity and optimize for microrover platforms. This paper sets out the various components of the modular SLAM system and then assesses their comparative performance using simulated data from the Planetary and Asteroid Natural Scene Generation Utility (PANGU), as well as real-world datasets from the West Wittering field trials (performed by the STAR Lab) and the SEEKER field trials in Chile (performed by the European Space Agency). The system as a whole was shown to perform reliably, with the best performance observed using a combination of Hou-saliency and speeded-up robust features (SURF) descriptors with an extended Kalman filter, which performed with higher accuracy than a state-of-the-art, independently optimized visual odometry localization system on a challenging real-world dataset

Ontology-Based Self-Reconfiguring Guidance, Navigation, and Control for Planetary Rovers

“…The acceptable localization error is below 10%. An alternative configuration is the modular planetary monocular simultaneous localization and mapping (PM-SLAM) [30], which has multiple component and parameter configurations. PM-SLAM uses more computational power, but generally the localization error is lower than VO.…”

Adaptive and intelligent navigation of autonomous planetary rovers — A survey

“…Bajpai et al [54] specifically addressed the problem of SLAM for planetary rovers using a biologically-inspired approach. Here a hybrid visual saliency model is developed to enable semantic feature detection using monocular images, which, as a result, reduces the complexity of the system.…”

Surface Navigation

“…Tracking across successive images can use the point-based features, but only the salient features need be stored in the map. Planetary Monocular SLAM [111] is an example of such an algorithm. The algorithm also uses monocular images as an input to the system to further reduce the system complexity.…”