Improving slip prediction on Mars using thermal inertia measurements

3D Radiometric Mapping by Means of LiDAR SLAM and Thermal Camera Data Fusion

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.

Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

Accurate classification and identification of the detected terrain is the basis for the long-distance patrol mission of the planetary rover. But terrain measurement based on vision and radar is subject to conditions such as light changes and dust storms. In this paper, under the premise of not increasing the sensor load of the existing rover, a terrain classification and recognition method based on vibration is proposed. Firstly, the time-frequency domain transformation of vibration information is realized by fast Fourier transform (FFT), and the characteristic representation of vibration information is given. Secondly, a deep neural network based on multi-layer perception is designed to realize classification of different terrains. Finally, combined with the Jackal unmanned vehicle platform, the XQ unmanned vehicle platform, and the vibration sensor, the terrain classification comparison test based on five different terrains was completed. The results show that the proposed algorithm has higher classification accuracy, and different platforms and running speeds have certain influence on the terrain classification at the same time, which provides support for subsequent practical applications.