Cesar Cadena

Autonomous, multi-modal robots are poised to revolutionize forestry by providing unprecedented tree-level data for optimized and sustainable forest management.

LiDAR odometry can be made significantly more robust in challenging, feature-poor environments by representing surfaces as compact voxel-wise oriented height images and focusing registration on geometrically informative regions.

Robots lost in the vineyard? Not anymore: encoding row-level semantics into a particle filter enables robust localization in repetitive agricultural environments where LiDAR and vision alone fail.