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Learning Heuristic Search via Imitation [article]

Mohak Bhardwaj, Sanjiban Choudhury, Sebastian Scherer
2017 arXiv   pre-print
Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding the search towards potentially good directions and consequently minimizing search effort. Moreover, it must infer such directions in an efficient manner using only the information
more » ... uncovered by the search up until that time. However, state of the art methods do not address the problem of computing a heuristic that explicitly minimizes search effort. In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand. Unfortunately, naively training such policies leads to slow convergence and poor local minima. We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search effort. We leverage the fact that such oracles can be efficiently computed using dynamic programming and derive performance guarantees for the learnt heuristic. We validate the approach on a spectrum of environments which show that SaIL consistently outperforms state of the art algorithms. Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time.
arXiv:1707.03034v1 fatcat:wvaf7se5kndohlisqlhphj7bra

Direct Monocular Odometry Using Points and Lines [article]

Shichao Yang, Sebastian Scherer
2017 arXiv   pre-print
Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments and is also more robust to lighting changes and fast motion by increasing the convergence basin. We
more » ... maintain a depth map for the keyframe then in the tracking part, the camera pose is recovered by minimizing both the photometric error and geometric error to the matched edge in a probabilistic framework. In the mapping part, edge is used to speed up and increase stereo matching accuracy. On various public datasets, our algorithm achieves better or comparable performance than state-of-the-art monocular odometry methods. In some challenging texture-less environments, our algorithm reduces the state estimation error over 50%.
arXiv:1703.06380v1 fatcat:youkvl3dlbfpfjydgmiltxxez4

TartanVO: A Generalizable Learning-based VO [article]

Wenshan Wang, Yaoyu Hu, Sebastian Scherer
2020 arXiv   pre-print
We present the first learning-based visual odometry (VO) model, which generalizes to multiple datasets and real-world scenarios and outperforms geometry-based methods in challenging scenes. We achieve this by leveraging the SLAM dataset TartanAir, which provides a large amount of diverse synthetic data in challenging environments. Furthermore, to make our VO model generalize across datasets, we propose an up-to-scale loss function and incorporate the camera intrinsic parameters into the model.
more » ... xperiments show that a single model, TartanVO, trained only on synthetic data, without any finetuning, can be generalized to real-world datasets such as KITTI and EuRoC, demonstrating significant advantages over the geometry-based methods on challenging trajectories. Our code is available at
arXiv:2011.00359v1 fatcat:niozq53upzfbjctn7sxe53n52i

LiDAR Enhanced Structure-from-Motion [article]

Weikun Zhen, Yaoyu Hu, Huai Yu, Sebastian Scherer
2019 arXiv   pre-print
Zhen is with the Department of Mechanical Engineering, Yaoyu Hu, Huai Yu and Sebastian Scherer are with the Robotics Institute.  ... 
arXiv:1911.03369v1 fatcat:k3buawb4fzew3acezsridb56oi

Bayesian Active Edge Evaluation on Expensive Graphs [article]

Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer
2017 arXiv   pre-print
Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic arm manipulating objects on a cluttered table top. State-of-the-art motion planning systems do not exploit this structure, thereby expending valuable planning effort searching for implausible solutions. We are interested in planning algorithms that actively infer the underlying structure of the valid configuration
more » ... during planning in order to find solutions with minimal effort. Consider the problem of evaluating edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, this challenge has been addressed via laziness i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard, but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters.
arXiv:1711.07329v1 fatcat:vf4tggyp2jfvzb5gn6vra4eqpa

A Stereo Algorithm for Thin Obstacles and Reflective Objects [article]

John Keller, Sebastian Scherer
2019 arXiv   pre-print
Stereo cameras are a popular choice for obstacle avoidance for outdoor lighweight, low-cost robotics applications. However, they are unable to sense thin and reflective objects well. Currently, many algorithms are tuned to perform well on indoor scenes like the Middlebury dataset. When navigating outdoors, reflective objects, like windows and glass, and thin obstacles, like wires, are not well handled by most stereo disparity algorithms. Reflections, repeating patterns and objects parallel to
more » ... e cameras' baseline causes mismatches between image pairs which leads to bad disparity estimates. Thin obstacles are difficult for many sliding window based disparity methods to detect because they do not take up large portions of the pixels in the sliding window. We use a trinocular camera setup and micropolarizer camera capable of detecting reflective objects to overcome these issues. We present a hierarchical disparity algorithm that reduces noise, separately identify wires using semantic object triangulation in three images, and use information about the polarization of light to estimate the disparity of reflective objects. We evaluate our approach on outdoor data that we collected. Our method contained an average of 9.27% of bad pixels compared to a typical stereo algorithm's 18.4% of bad pixels in scenes containing reflective objects. Our trinocular and semantic wire disparity methods detected 53% of wire pixels, whereas a typical two camera stereo algorithm detected 5%.
arXiv:1910.04874v1 fatcat:nk4no5wlgve45hgmgdlm56l2bm

Adaptive Information Gathering via Imitation Learning [article]

Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade, Sebastian Scherer, Debadeepta Dey
2017 arXiv   pre-print
In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such policies heavily depends on the geometric distribution of objects in the world. On the other hand, the principled approach of employing online POMDP solvers is rendered impractical
more » ... by the need to explicitly sample online from a posterior distribution of world maps. We present a novel data-driven imitation learning framework to efficiently train information gathering policies. The policy imitates a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute maximally informative sensing locations. We analyze the learnt policy by showing that offline imitation of a clairvoyant oracle is implicitly equivalent to online oracle execution in conjunction with posterior sampling. This observation allows us to obtain powerful near-optimality guarantees for information gathering problems possessing an adaptive sub-modularity property. As demonstrated on a spectrum of 2D and 3D exploration problems, the trained policies enjoy the best of both worlds - they adapt to different world map distributions while being computationally inexpensive to evaluate.
arXiv:1705.07834v1 fatcat:wou22oidrvckjbncjvbur6c5x4

Open Problems in Robotic Anomaly Detection [article]

Ritwik Gupta, Zachary T. Kurtz, Sebastian Scherer, Jonathon M. Smereka
2018 arXiv   pre-print
Kurtz are with the Software Engineering Institute, Carnegie Mellon University, 4500 Fifth Avenue, Pittsburgh, PA 15213, USA {rgupta, ztkurtz} 2 Sebastian Scherer is with the Robotics Institute  ... 
arXiv:1809.03565v1 fatcat:xadvrc7yfffo3lhtcetb5575jq

Arthroskopisch assistierte Behandlung von Tibiakopffrakturen

Michael Scherer, Sebastian Riha
2007 OP-Journal  
Scherer, Sebastian Riha Zusammenfassung Tibiakopffrakturen stellen hohe Anforderungen an den Chirurgen.  ... 
doi:10.1055/s-2007-984719 fatcat:bi33nrnm5jd3birv6hja63557a

Semantic 3D Occupancy Mapping through Efficient High Order CRFs [article]

Shichao Yang, Yulan Huang, Sebastian Scherer
2017 arXiv   pre-print
Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and
more » ... ationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.
arXiv:1707.07388v1 fatcat:uxgaebfdz5ekrfaal43ujftzx4

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression [article]

Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
2021 arXiv   pre-print
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads
more » ... rs to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples.
arXiv:2105.02796v1 fatcat:dpdkry3jbbfudjflvrgytovzky

A Unified 3D Mapping Framework using a 3D or 2D LiDAR [article]

Weikun Zhen, Sebastian Scherer
2018 arXiv   pre-print
Simultaneous Localization and Mapping (SLAM) has been considered as a solved problem thanks to the progress made in the past few years. However, the great majority of LiDAR-based SLAM algorithms are designed for a specific type of payload and therefore don't generalize across different platforms. In practice, this drawback causes the development, deployment and maintenance of an algorithm difficult. Consequently, our work focuses on improving the compatibility across different sensing payloads.
more » ... Specifically, we extend the Cartographer SLAM library to handle different types of LiDAR including fixed or rotating, 2D or 3D LiDARs. By replacing the localization module of Cartographer and maintaining the sparse pose graph (SPG), the proposed framework can create high-quality 3D maps in real-time on different sensing payloads. Additionally, it brings the benefit of simplicity with only a few parameters need to be adjusted for each sensor type.
arXiv:1810.12515v1 fatcat:wibqjg2eafeifhwa7q2j355pni

Data-driven Planning via Imitation Learning [article]

Sanjiban Choudhury, Mohak Bhardwaj, Sankalp Arora, Ashish Kapoor, Gireeja Ranade, Sebastian Scherer, Debadeepta Dey
2017 arXiv   pre-print
Arora and Scherer [5] use an efficient TSP with a random sampling approach. 3) Problems with Hidden World Maps: We now consider the setting where the world map φ is hidden.  ... 
arXiv:1711.06391v1 fatcat:2nltaus5bfhdldnuequtw3c4fe

Toward Efficient and Robust Multiple Camera Visual-inertial Odometry [article]

Yao He, Huai Yu, Wen Yang, Sebastian Scherer
2021 arXiv   pre-print
Efficiency and robustness are the essential criteria for the visual-inertial odometry (VIO) system. To process massive visual data, the high cost on CPU resources and computation latency limits VIO's possibility in integration with other applications. Recently, the powerful embedded GPUs have great potentials to improve the front-end image processing capability. Meanwhile, multi-camera systems can increase the visual constraints for back-end optimization. Inspired by these insights, we
more » ... te the GPU-enhanced algorithms in the field of VIO and thus propose a new front-end with NVIDIA Vision Programming Interface (VPI). This new front-end then enables multi-camera VIO feature association and provides more stable back-end pose optimization. Experiments with our new front-end on monocular datasets show the CPU resource occupation rate and computational latency are reduced by 40.4% and 50.6% without losing accuracy compared with the original VIO. The multi-camera system shows a higher VIO initialization success rate and better robustness overall state estimation.
arXiv:2109.12030v1 fatcat:s7kvadgf2zedthwdyrekmtureq

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning [article]

Brady Moon, Satrajit Chatterjee, Sebastian Scherer
2022 arXiv   pre-print
Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This
more » ... is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces.
arXiv:2203.12830v1 fatcat:xlh53ba6prf4zkkfuovx3snjbi
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