Author: Dimitrios Geromichalos
Year: 2019
- relative positionning via scan matching + global matching of local map to an orbiter global map
- Factor graph SLAM for planetary exploration : Adaptive localization and mapping with application to planetary rovers
- " However, LiDARs are hardly used in planetary rovers due to the heavy weight and high power consumption and to the authors knowledge , there exists no LiDAR sensor qualified for planetary missions. "
- No dense 3D reconstruction needed, just 2,5D elevation grid map
- for local mapping, scan to map with a particle filter combined with ICP
- for global pose, use template matching wrt orbital images
- PC are downsampled, transformed in the global map frame, cropped and the uncertainty of the eight of each point is computed using a sensor model
- Then update the local map (whose cells also have a 1D Gaussian distribution in z) using a Kalman Filtering operation
- Why map update is done before pose estimation?
- The scan matching particle filter estimates
$[x, y, \theta]$ - Use a particle filter where the weights of each particle is computed using the fitness score of a single ICP iteration
- The estimated pose is then the average of the particles with the highest weights
- template matching of local elevation map and global orbital map
- Uses the sobel operator to generate gradient images
- template matching cannot deal with rotated images: 20 rotated images are generated
- pose registration limited to the orbital view resolution
- the surroundings must be rich in elevation features
- data collected in tenerife
- a flying drone produced the DEM for the global map matching