Illumination Robust Monocular Direct Visual Odometry for Outdoor Environment Mapping

Wu, Xiaolong; Pradalier, Cédric

doi:10.1109/icra.2019.8793607

Cited by 19 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method based on the census transform Alismail, Browning, and Lucey (2017) uses a binary descriptor to achieve local illumination invariance during the motion estimation. Based on the analysis from our previous work (Wu & Pradalier, 2019), we found the affine‐model‐based approach shows both good accuracy and large convergence basin, which can be used to formulate our inter‐camera tracking algorithm.…”

Section: Related Workmentioning

confidence: 95%

Robotic weed control using automated weed and crop classification

Aravecchia

Lottes

et al. 2020

Journal of Field Robotics

Self Cite

125

View full text Add to dashboard Cite

Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant‐time weed detection systems to achieve real‐time treatment, which forecloses the implementation of more capable but time‐consuming algorithms, for example, learning‐based methods. In this paper, a nonoverlapping multicamera system is applied to provide flexibility for the weed control system in dealing with the indeterminate classification delays. The design, implementation, and testing of our proposed modular weed control unit with mechanical and chemical weeding tools are presented. A framework that performs naive Bayes filtering, 3D direct intra‐ and inter‐camera visual tracking, and predictive control, while integrating state‐of‐the‐art crop/weed detection algorithms, is developed to guide the tools to achieve high‐precision weed removal. The experimental results show that our proposed fully operational weed control system is capable of performing selective mechanical as well as chemical in‐row weeding with indeterminate detection delays in different terrain conditions and crop growth stages.

show abstract

Section: Related Workmentioning

confidence: 95%

Robotic weed control using automated weed and crop classification

Aravecchia

Lottes

et al. 2020

Journal of Field Robotics

Self Cite

125

View full text Add to dashboard Cite

show abstract

“…The task of trees crown reconstruction remains unsolved due to the variability of trees phenotype, with some research focused on CAD model replacement [8] or radius estimation from flying laser measurements [15]. Additionally, works such as [26] and [27] show that it might be easier to model tree structure such as a trunk, branches and twigs when they are exposed and observable, such as during winter when the trees are bare. Another challenge is meshing the point clouds to create surfaces that are suitable for autonomous robot navigation such as path planning, with few libraries such as [21] and [24] providing useful meshing tools.…”

Section: Related Workmentioning

confidence: 99%

Mapping in unstructured natural environment: a sensor fusion framework for wearable sensor suites

et al. 2021

View full text Add to dashboard Cite

We present a generalized mapping framework that can withstand the challenges incurred by working in unstructured outdoor environments, such as a snowy forest. The proposed method takes advantage of a sensor fusion scheme, where sensors such as cameras and lidars are used in order to reconstruct the surrounding natural environment. Although mapping techniques such as SLAM and ICP cannot themselves properly handle the complexity of natural scenes, they do have the potential to contribute to the global solution in a proposed sensor fusion scheme, based on a factor graph architecture. In this paper, we propose an innovative map registration scheme for visual maps, and show how it can improve the reconstruction quality after data fusion. We also analyze the behavior and sensitivity of factor graphs to uncertainties, by comparing the residual error with different parameter combinations such as variances, using an exhaustive grid search with ground truth comparison. Finally, we suggest an ICP-inferred loop closure, capable of compensating position and attitude drift. The experiments are carried out by recording in a snowy forest using a wearable sensor suite. In the experiments, ground truth was acquired using a millimeter-accurate total station. The proposed framework is shown to be robust and likewise capable of providing estimates that are otherwise unattainable using classic techniques, such as visual SLAM and ICP for lasers. Finally, a visible improvement in the map reconstruction quality is shown, and the proposed framework achieves a translation error of 0.36 m.

show abstract

“…For example, upgrading SIFT Flow to use learned features (Benbihi et al, 2019; Kim et al, 2017a), or other features specifically designed and trained for data from a natural environment (Olid et al, 2018), upgrading the dense correspondence framework itself (with, e.g., 3DCC; see Zhou et al, 2016), or designing our own may be promising ways to boost image alignment performance. Another boost may be possible if the KLT feature tracks that provided the map points are replaced (with, e.g., Ilg et al, 2017; Wu and Pradalier, 2018). Furthermore, Chahine and Pradalier (2018) demonstrated semi-dense visual SLAM on the Symphony Lake Dataset, which could later be used with ICP (similar to, e.g., Park et al, 2019) to provide another source of loop closures between surveys.…”

Section: Future Workmentioning

confidence: 99%

Transforming multiple visual surveys of a natural environment into time-lapses

Griffith

Dellaert

Pradalier

2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

This article presents a new framework to help transform visual surveys of a natural environment into time-lapses. As data association across year-long variation in appearance continues to represent a formidable challenge, we present success with a map-centric approach, which builds on 3D vision for visual data association. We use a foundation of map point priors and geometric constraints within a dense correspondence image alignment optimization to align images and acquire loop closures between surveys. This framework produces many loop closures between sessions. Outlier loop closures are filtered in the frontend and in the backend to improve robustness. From the result map, the Reprojection Flow algorithm is applied to create time-lapses. The evaluation of our framework on the Symphony Lake Dataset, which has considerable variation in appearance, led to year-long time-lapses of many different scenes. In comparison with another approach based on using iterative closest point (ICP) plus a homography, our framework produced more and better-quality alignments. With many scenes of the 1.3 km environment consistently aligning well in random image pairs, we next produced 100 time-lapses across 37 surveys captured in a year. Approximately one-third had at least 20 (out of usually 33) well-aligned images, which spanned all four seasons. With promising results, we evaluated the pose error of misaligned image pairs and found that improving map consistency could lead to even better results.

show abstract

Illumination Robust Monocular Direct Visual Odometry for Outdoor Environment Mapping

Cited by 19 publications

References 22 publications

Robotic weed control using automated weed and crop classification

Robotic weed control using automated weed and crop classification

Mapping in unstructured natural environment: a sensor fusion framework for wearable sensor suites

Transforming multiple visual surveys of a natural environment into time-lapses

Contact Info

Product

Resources

About