Adaptive Robust Kernels for Non-Linear Least Squares Problems

Chebrolu, Nived; Läbe, Thomas; Vysotska, Olga; Behley, Jens; Stachniss, Cyrill

doi:10.1109/lra.2021.3061331

Cited by 51 publications

(30 citation statements)

References 21 publications

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“…This type of approach is used in early work in dynamic covariance scaling [75] and related methods such as switchable constraints [76], [77] and max mixtures [78], which use a tunable weighting to decrease the influence of inconsistent loop closures on the optimization. In recent work, adaptive kernels for robust cost functions have been developed [79]. There are also distinct methods that check for, and exclude, inconsistent loop closures from the optimization, such as realizing, reversing, recovering (RRR) [80].…”

Section: The Slam Back-end: Robot Pose and Map Estimationmentioning

confidence: 99%

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

et al. 2021

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At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot's position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environment.

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Section: The Slam Back-end: Robot Pose and Map Estimationmentioning

confidence: 99%

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

et al. 2021

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show abstract

“…Also, their performance depends strongly on the choice of specific parameters, which often leads to manual tuning. In more recent work like [7] and [8], the parametrization issue was resolved by self-tuning algorithms based on expectation-maximization. But still, they are limited by their symmetry constraint.…”

Section: Prior Workmentioning

confidence: 99%

Advancing Mixture Models for Least Squares Optimization

Pfeifer

Lange

Protzel

2021

IEEE Robot. Autom. Lett.

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Gaussian mixtures are a powerful and widely used tool to model non-Gaussian estimation problems. They are able to describe measurement errors that follow arbitrary distributions and can represent ambiguity in assignment tasks like point set registration or tracking. However, using them with common least squares solvers is still difficult. Existing approaches are either approximations of the true mixture or prone to convergence issues due to their strong nonlinearity. We propose a novel least squares representation of a Gaussian mixture, which is an exact and almost linear model of the corresponding log-likelihood. Our approach provides an efficient, accurate and flexible model for many probabilistic estimation problems and can be used as cost function for least squares solvers. We demonstrate its superior performance in various Monte Carlo experiments, including different kinds of point set registration. Our implementation is available as open source code for the state-of-the-art solvers Ceres and GTSAM.

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“…where x = [ x m y m z m u v w r 0 ] T is the optimization variable and p i is a given inlier point. Meanwhile, to avoid a refined axis being weakened by few large residuals, a robust kernel is employed, e.g., the Huber kernel [36],…”

Section: Algorithm 2: Refinement Of the Coarse Axismentioning

confidence: 99%

A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

Can

Chen

et al. 2021

Sensors

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In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

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Adaptive Robust Kernels for Non-Linear Least Squares Problems

Cited by 51 publications

References 21 publications

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

Advancing Mixture Models for Least Squares Optimization

A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

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