Abstract
To enhance sensor capabilities, sensor data readings from different modalities must be fused. The main contribution of this paper is to present a sensor data fusion approach that can reduce KinectTM sensor limitations. This approach involves combining laser with KinectTM sensors. Sensor data is modelled in a 3D environment based on octrees using a probabilistic occupancy estimation. The Bayesian method, which takes into account the uncertainty inherent in the sensor measurements, is used to fuse the sensor information and update the 3D octree map. The sensor fusion yields a significant increase of the field of view of the KinectTM sensor that can be used for robot tasks.
Highlights
Fusion of sensory information is essential in the field of mobile robots
The main contribution of this paper is to focus on the problem of fusing range readings from a laser device with a depth KinectT M image in order to increase the field of view and reduce the minimum range of the KinectT M sensor
This approach demonstrates that by fusing the KinectT M with laser sensor data sets, the KinectT M improves its field of view as well as its minimum close range detection
Summary
Fusion of sensory information is essential in the field of mobile robots The former is necessary in order to achieve full autonomy and widen the range of its applicability. Conversion of sensor data from the physical measurements to the internal representation should be easy to carry out In this context, the map should be expanded as needed and must have multiple resolution for different mobile robot tasks. The main contribution of this paper is to focus on the problem of fusing range readings from a laser device with a depth KinectT M image in order to increase the field of view and reduce the minimum range of the KinectT M sensor. It provides data by a Hokuyo laser range finder and a Microsoft KinectT M.
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