In this work we have performed a sensitivity analysis of the Visual Position Estimator for Rover (VIPER) algorithm using data and images provided by NASA MER exploration rovers and NASA Mars Reconnaissance Orbiter. The algorithm retrieves the rover camera position and orientation relative to a Digital Elevation Model by comparing the skyline extracted from a panoramic image captured by the rover and a set of skylines simulated on a template positions grid over the DEM. This algorithm can be used to initialize the rover position after landing in a Mars Body-Fixed Frame and as verification of rover guidance and navigation outputs. In order to test the algorithm performances we have processed data and images provided by NASA Mars Exploration Rover PANCAM and DEM provided by NASA Mars Reconnaissance Orbiter HiRISE telescope. The sensitivity analysis has been performed by varying DEM resolution and template positions density. In the tested cases we show that this localization technique achieves an error up to 50 [m], thus it is possible to decrease the position uncertainty estimated with other localization techniques, like the Entry Descent and Landing estimation.

The ability to produce 3D maps with infrared radiometric information is of great interest for many applications, such as rover navigation, industrial plant monitoring, and rescue robotics. In this paper, we present a system for large-scale thermal mapping based on IR thermal images and 3D LiDAR point cloud data fusion. The alignment between the point clouds and the thermal images is carried out using the extrinsic camera-to-LiDAR parameters, obtained by means of a dedicated calibration process. Rover’s trajectory, which is necessary for point cloud registration, is obtained by means of a LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. Finally, the registered and merged thermal point clouds are represented through an OcTree data structure, where each voxel is associated with the average temperature of the 3D points contained within. Furthermore, the paper presents in detail the method for determining extrinsic parameters, which is based on the identification of a hot cardboard box. Both methods were validated in a laboratory environment and outdoors. It is shown that the developed system is capable of locating a thermal object with an accuracy of up to 9 cm in a 45 m map size with a voxelization of 14 cm.

Ego-motion estimation and awareness of the surroundings are crucial tasks to perform for an exploration vehicle. Visual SLAM techniques allow to fulfill these needs by extracting meaningful information from images: changes in the appearance of the environments are used to simultaneously estimate both its structure and the relative motion of the camera. While tipically heavyweight and computationally complex, thanks to the most recent advances in embedded platform architecture, most of the available open source Visual SLAM software can be run online on ARM processors. This, together with miniaturization and lower power consumption, opens great scenarios for autonomous navigation of mobile robots. This paper covers the implementation of most of the available open source Visual Stereo SLAM on a nVidia Jetson TX2 platform highlighting critical information such as pose estimation accuracy, capability of loop closure, CPU and memory usage. The measurement setup is mounted on the MORPHEUS (Mars Operative Rover of Padova Engineering University Students) rover and LiDAR data is used as reference for performance evaluation.