Efficient autonomous navigation for planetary rovers with limited resources

Gerdes, Levin; Azkarate, Martín; Sánchez‐Ibáñez, José Ricardo; Joudrier, Luc; Pérez-del-Pulgar, Carlos Jesús

doi:10.1002/rob.21981

Cited by 22 publications

(16 citation statements)

References 20 publications

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“…First, we execute motion planning using sensor-generated, incomplete DEMs while not planning any path through areas without elevation information. Currently, this baseline scenario is very common in literature [36], [37]. Our experiments show that this approach cannot plan far-ahead and that the paths are not cost-optimized over the entire map.…”

Section: G Preliminary Study On Motion Planningmentioning

confidence: 98%

Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning

Stolzle

Miki

Gerdes

et al. 2022

IEEE Robot. Autom. Lett.

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Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing values in the elevation map are filled-in using traditional interpolation, diffusion or patch-matching techniques. These methods cannot leverage the high-level terrain characteristics and the geometric constraints of line of sight we humans use intuitively to predict occluded areas. We introduce a self-supervised learning approach capable of training on real-world data without a need for ground-truth information to reconstruct the occluded areas in the DEMs. We accomplish this by adding artificial occlusion to the incomplete elevation maps constructed on a real robot by performing ray casting. We first evaluate a supervised learning approach on synthetic data for which we have the full ground-truth available and subsequently move to several real-world datasets. These real-world datasets were recorded during exploration of both structured and unstructured terrain with a legged robot, and additionally in a planetary scenario on Lunar analogue terrain. We state a significant improvement compared to the baseline methods both on synthetic terrain and for the real-world datasets. Our neural network is able to run in real-time on both CPU and GPU with suitable sampling rates for autonomous ground robots. We motivate the applicability of reconstructing occlusion in elevation maps with preliminary motion planning experiments.

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Section: G Preliminary Study On Motion Planningmentioning

confidence: 98%

Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning

Stolzle

Miki

Gerdes

et al. 2022

IEEE Robot. Autom. Lett.

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“…The Katwijk beach planetary rover dataset is a widely used dataset for navigation vision of the planetary rovers [48][49][50][51][52][53]. The Katwijk dataset is a proper dataset as the practical condition of planetary exploration.…”

Section: The Katwijk Beach Planetary Rover Datasetmentioning

confidence: 99%

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Kuang

Rana

Zhao

2021

Sensors

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Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

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“…The visual navigation dataset of the planetary rovers used in this research is part1 and part2 of the Katwijk beach planetary rover dataset [49] from the European Space Agency (ESA) [50][51][52][53], which contains 2250 frames of the image. The Katwijk dataset is a professional open dataset for the navigation vision of the planetary rover research, and many studies use the Katwijk dataset as the planetary environment [44,52,54].…”

Section: The Real-life Visual Navigation Dataset For the Planetary Roversmentioning

confidence: 99%

Rock Segmentation in the Navigation Vision of the Planetary Rovers

et al. 2021

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Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

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Efficient autonomous navigation for planetary rovers with limited resources

Cited by 22 publications

References 20 publications

Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning

Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Rock Segmentation in the Navigation Vision of the Planetary Rovers

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