Data fusion strategies for hazard detection and safe site selection for planetary and small body landings

F. Câmara, J. Oliveira, T. Hormigo, J. Araújo, R. Ribeiro, A. Falcão, M. Gomes, O. Dubois-Matra, S. Vijendran

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This paper discusses the design and evaluation of data fusion strategies to perform tiered fusion of several heterogeneous sensors and a priori data. The aim is to increase robustness and performance of hazard detection and avoidance systems, while enabling safe planetary and small body landings anytime, anywhere. The focus is on Mars and asteroid landing mission scenarios and three distinct data fusion algorithms are introduced and compared. The first algorithm consists of a hybrid camera–LIDAR hazard detection and avoidance system, the H2DAS, in which data fusion is performed at both sensor-level data (reconstruction of the point cloud obtained with a scanning LIDAR using the navigation motion states and correcting the image for motion compensation using IMU data), feature-level data (concatenation of multiple digital elevation maps, obtained from consecutive LIDAR images, to achieve higher accuracy and resolution maps while enabling relative positioning) as well as decision-level data (fusing hazard maps from multiple sensors onto a single image space, with a single grid orientation and spacing). The second method presented is a hybrid reasoning fusion, the HRF, in which innovative algorithms replace the decision-level functions of the previous method, by combining three different reasoning engines—a fuzzy reasoning engine, a probabilistic reasoning engine and an evidential reasoning engine—to produce safety maps. Finally, the third method presented is called Intelligent Planetary Site Selection, the IPSIS, an innovative multi-criteria, dynamic decision-level data fusion algorithm that takes into account historical information for the selection of landing sites and a piloting function with a non-exhaustive landing site search capability, i.e., capable of finding local optima by searching a reduced set of global maps. All the discussed data fusion strategies and algorithms have been integrated, verified and validated in a closed-loop simulation environment. Monte Carlo simulation campaigns were performed for the algorithms performance assessment and benchmarking. The simulations results comprise the landing phases of Mars and Phobos landing mission scenarios.

Original languageEnglish
Pages (from-to)271-290
Number of pages20
JournalCEAS Space Journal
Issue number2
Publication statusPublished - 17 Jun 2015
Event9th International ESA Conference on Guidance, Navigation and Control Systems - Porto, Portugal
Duration: 2 Jun 20146 Jun 2014


  • Evidential reasoning
  • Fuzzy reasoning
  • Hazard detection and avoidance
  • Multi-sensor information fusion
  • Probabilistic reasoning
  • Safe planetary landing
  • Sensor data fusion


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