Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing...
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The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research ha...
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Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, colli...
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Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-...
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ISBN:
(纸本)9781665481106
Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images.
For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lack...
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ISBN:
(纸本)9781665481106
For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR.
This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50...
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The precise estimation of system states is essential for the locomotion control of biped robots to maintain balance. Currently, the estimation of system states is either based on vision data that is susceptible to the...
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Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor on...
Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.
Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing...
详细信息
ISBN:
(纸本)9781665481106
Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.
Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cove...
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ISBN:
(纸本)9781728190495
Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil
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