Refine
Document Type
Language
- English (2)
Has Fulltext
- no (2)
Reviewed
Keywords
Is part of the Bibliography
- yes (2)
Simultaneous 3D Reconstruction and Vegetation Classification Utilizing a Multispectral Stereo Camera
(2023)
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.
In order to allow robust obstacle detection for autonomous freight traffic using freight trains or shunting locomotives, several different sensors are required. Humans and other objects must be detected so that the vehicle can stop in time. Laser scanners deliver distance information and are popular in robotics and automation. Cameras deliver further pieces of information on the environment and are especially useful for the classification of objects, but do not deliver distance measurements. Thermal cameras are ideal for the detection of humans based on their body temperature if the surrounding temperature is not too similar. It is only the combination of these different sensors which delivers enough robustness. Therefore a sensor fusion and an extrinsic calibration has to take place. This article presents an approach fusing a 2D and an 8-layer 3D laser scanner with a thermal and a Red-Green-Blue (RGB) camera, using a triangular calibration target taking all six degrees of freedom into account. The calibration was tested and the results validated during reference measurements and autonomous and manually controlled field tests. This sensor fusion approach was used for the obstacle detection of an autonomous shunting locomotive.