10 research outputs found
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
Bird's Eye View (BEV) representations are tremendously useful for
perception-related automated driving tasks. However, generating BEVs from
surround-view fisheye camera images is challenging due to the strong
distortions introduced by such wide-angle lenses. We take the first step in
addressing this challenge and introduce a baseline, F2BEV, to generate
discretized BEV height maps and BEV semantic segmentation maps from fisheye
images. F2BEV consists of a distortion-aware spatial cross attention module for
querying and consolidating spatial information from fisheye image features in a
transformer-style architecture followed by a task-specific head. We evaluate
single-task and multi-task variants of F2BEV on our synthetic FB-SSEM dataset,
all of which generate better BEV height and segmentation maps (in terms of the
IoU) than a state-of-the-art BEV generation method operating on undistorted
fisheye images. We also demonstrate discretized height map generation from
real-world fisheye images using F2BEV. Our dataset is publicly available at
https://github.com/volvo-cars/FB-SSEM-datasetComment: Accepted for publication in the proceedings of IEEE/RSJ International
Conference on Intelligent Robots and Systems 202
Spiral Complete Coverage Path Planning Based on Conformal Slit Mapping in Multi-connected Domains
Generating a smooth and shorter spiral complete coverage path in a
multi-connected domain is an important research area in robotic cavity
machining. Traditional spiral path planning methods in multi-connected domains
involve a subregion division procedure; a deformed spiral path is incorporated
within each subregion, and these paths within the subregions are interconnected
with bridges. In intricate domains with abundant voids and irregular
boundaries, the added subregion boundaries increase the path avoidance
requirements. This results in excessive bridging and necessitates longer
uneven-density spirals to achieve complete subregion coverage. Considering that
conformal slit mapping can transform multi-connected regions into regular disks
or annuluses without subregion division, this paper presents a novel spiral
complete coverage path planning method by conformal slit mapping. Firstly, a
slit mapping calculation technique is proposed for segmented cubic spline
boundaries with corners. Then, a spiral path spacing control method is
developed based on the maximum inscribed circle radius between adjacent
conformal slit mapping iso-parameters. Lastly, the spiral path is derived by
offsetting iso-parameters. The complexity and applicability of the proposed
method are comprehensively analyzed across various boundary scenarios.
Meanwhile, two cavities milling experiments are conducted to compare the new
method with conventional spiral complete coverage path methods. The comparation
indicate that the new path meets the requirement for complete coverage in
cavity machining while reducing path length and machining time by 12.70% and
12.34%, respectively.Comment: This article has not been formally published yet and may undergo
minor content change
Monte Carlo Filtering Objectives
Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. MCFOs extend the choices of likelihood estimators beyond Sequential Monte Carlo in state-of-the-art objectives, possess important properties revealing the factors for the tightness of objectives, and allow for less biased and variant gradient estimates. We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data
Amortized Variational Inference for Road Friction Estimation
Road friction estimation concerns inference of the coefficient between the tire and road surface to facilitate active safety features. Current state-of-the-art methods lack generalization capability to cope with different tire characteristics and models are restricted when using Bayesian inference in estimation while recent supervised learning methods lack uncertainty prediction on estimates. This paper introduces variational inference to approximate intractable posterior of friction estimates and learns an amortized variational inference model from tire measurement data to facilitate probabilistic estimation while sustaining the flexibility of tire models. As a by-product, a probabilistic tire model can be learned jointly with friction estimator model. Experiments on simulated and field test data show that the learned friction estimator provides accurate estimates with robust uncertainty measures in a wide range of tire excitation levels. Meanwhile, the learned tire model reflects well-studied tire characteristics from field test data