Platform for evaluating sensors and human detection in autonomous mowing operations


The concept of autonomous farming concerns automatic agricultural machines operating safely and efficiently without human intervention. In order to ensure safe autonomous operation, real-time risk detection and avoidance must be undertaken. This paper presents a flexible vehicle-mounted sensor system for recording positional and imaging data with a total of six sensors, and a full procedure for calibrating and registering all sensors. Authentic data were recorded for a case study on grass-harvesting and human safety. The paper incorporates parts of ISO 18497 (an emerging standard for safety of highly automated machinery in agriculture) related to human detection and safety. The case study investigates four different sensing technologies and is intended as a dataset to validate human safety or a human detection system in grass-harvesting. The study presents common algorithms that are able to detect humans, but struggle to handle lying or occluded humans in high grass.

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This research is sponsored by the Innovation Fund Denmark as part of the Project “SAFE - Safer Autonomous Farming Equipment” (Project No. 16-2014-0) and “Multi-sensor system for ensuring ethical and efficient crop production” (Project No. 155-2013-6).

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Correspondence to P. Christiansen.

Appendix: Thermal–visual registration and evaluation

Appendix: Thermal–visual registration and evaluation

First a total of 47 thermal and stereo synchronized images were selected from a single calibration recording. For each image, a rectangle area inside the checkerboard was marked manually to specify an image cropping, see Fig. 11. For RGB images, the cropped image was converted to the LAB color space and a Gaussian mixture model separated the pixels into two clusters (copper and non-copper areas). The posterior probability of belonging to one of the Gaussian clusters was determined for all pixels in the original image, see Fig. 12. For thermal images, the cropped image was normalized—transforming pixel values in the range [0 1] by shifting and scaling. The same normalization was applied to the whole thermal image, see Fig. 13. The MATLAB calibration toolbox was able to automatically detect checkerboards of the transformed RGB and thermal images. The calibration toolbox was able to detect the checkerboard in 27 and 43 out of the 45 images for respectively stereo and thermal images. The 27 stereo images were used for calibrating the intrinsic and extrinsic parameters of the stereo camera. The 43 thermal images were used for determining the intrinsic parameters of the thermal camera.

Fig. 11

Image example and a manually marked rectangle

Fig. 12

Posterior probability of belonging to one of the Gaussian clusters for all pixels in the image example. Checkerboard detection is marked with blue crosses (Color figure online)

Fig. 13

Thermal image is normalized relative to the checkerboard. Checkerboard detection is marked with blue crosses (Color figure online)

Fig. 14

Zoomed images. Blue crosses mark corners detected by the MATLAB calibration toolbox for both an RGB image (left) and a thermal image (right). The red crosses (left) show how 3D points are projected to the thermal camera (Color figure online)

In 25 out of 47 synchronized images, the checkerboard was successfully detected by the MATLAB calibration toolbox for both RGB and thermal images. The toolbox estimated the 3D position of the checkerboard in all 25 images for each camera. The extrinsic parameters of the thermal camera were determined as the least square rigid transformation that mapped the estimated checkerboards from the left RGB camera to the thermal camera (in 3D).

The registration was evaluated on the 25 images to provide a quantitative evaluation of the thermal–visual registration. The camera calibration for the left stereo camera estimated—as already described—the checkerboard positions in 3D. These positions were then projected to the thermal image using the estimated extrinsic and intrinsic parameters of the thermal camera, see Fig. 4 (right).

The error was determined as the distance between the detected checkerboard and the projected 3D positions. Figure 15 shows the mean pixel error for each of the 25 images and the mean pixel error across all images on 4.66 pixels. The image example used in Figs. 11, 12, 13, and 14 is image 21 with a mean pixel error close to the mean pixel error across all images.

Fig. 15

The mean pixel error for 25 images (blue bars) and the mean pixel error across all images (red line) (Color figure online)

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Christiansen, P., Kragh, M., Steen, K.A. et al. Platform for evaluating sensors and human detection in autonomous mowing operations. Precision Agric 18, 350–365 (2017).

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  • Safe farming
  • Sensor platform
  • Object detection
  • Computer vision
  • ISO 18497
  • Autonomous farming