%0 Conference Paper %1 SSRR2023 %A Vollet, J. %A May, S. %A Nüchter, A. %B IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR '23) %D 2023 %K imported myown %P 132--138 %R 10.1109/SSRR59696.2023.10499928 %T Simultaneous 3D Reconstruction and Vegetation Classification Utilizing a Multispectral Stereo Camera %X Obstacle detection is crucial for ensuring the safety of autonomous robots and their surroundings in unstructured outdoor environments. Objects with minimal lateral dimensions can pose risks to the robot or serve as important elements in the infrastructure it operates in. Detecting these structures becomes particularly challenging when tall vegetation is present. Distinguishing between soft, traversable objects, such as tufts of grass, and potentially lethal solid obstacles is paramount to a robot’s ability to operate. This paper presents a novel approach that focuses on point cloud generation and vegetation identification to facilitate the safe navigation of autonomous outdoor robots. Our approach uses a single multispectral stereo camera system that employs a novel stereo matching strategy based on binary descriptors for spectrally non-identical image pairs.

@inproceedings{SSRR2023, abstract = { Obstacle detection is crucial for ensuring the safety of autonomous robots and their surroundings in unstructured outdoor environments. Objects with minimal lateral dimensions can pose risks to the robot or serve as important elements in the infrastructure it operates in. Detecting these structures becomes particularly challenging when tall vegetation is present. Distinguishing between soft, traversable objects, such as tufts of grass, and potentially lethal solid obstacles is paramount to a robot’s ability to operate. This paper presents a novel approach that focuses on point cloud generation and vegetation identification to facilitate the safe navigation of autonomous outdoor robots. Our approach uses a single multispectral stereo camera system that employs a novel stereo matching strategy based on binary descriptors for spectrally non-identical image pairs. }, added-at = {2024-07-08T21:51:43.000+0200}, author = {Vollet, J. and May, S. and N{\"u}chter, A.}, biburl = {https://www.bibsonomy.org/bibtex/259fdcf6aaeb80fb67394159031e5ef59/nuechter76}, booktitle = {IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR '23)}, doi = {10.1109/SSRR59696.2023.10499928}, interhash = {515b6b288cb899b65f05698b1d655b96}, intrahash = {59fdcf6aaeb80fb67394159031e5ef59}, keywords = {imported myown}, pages = {132--138}, timestamp = {2024-07-30T17:23:47.000+0200}, title = {Simultaneous 3D Reconstruction and Vegetation Classification Utilizing a Multispectral Stereo Camera}, year = 2023 }