Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation

• URL: http://arxiv.org/abs/2410.08687v1
• Date: Fri, 11 Oct 2024 10:19:24 GMT
• Title: Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation
• Authors: Hanieh Shojaei, Qianqian Zou, Max Mehltretter,
• Abstract summary: Safe navigation in new environments requires autonomous vehicles and robots to accurately interpret their surroundings. We propose a method to distinguish in-distribution (ID) from out-of-distribution (OOD) samples. We quantify both epistemic and aleatoric uncertainties using the feature space of a single deterministic model.
• Score: 0.6144680854063939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe navigation in new environments requires autonomous vehicles and robots to accurately interpret their surroundings, relying on LiDAR scene segmentation, out-of-distribution (OOD) obstacle detection, and uncertainty computation. We propose a method to distinguish in-distribution (ID) from OOD samples and quantify both epistemic and aleatoric uncertainties using the feature space of a single deterministic model. After training a semantic segmentation network, a Gaussian Mixture Model (GMM) is fitted to its feature space. OOD samples are detected by checking if their squared Mahalanobis distances to each Gaussian component conform to a chi-squared distribution, eliminating the need for an additional OOD training set. Given that the estimated mean and covariance matrix of a multivariate Gaussian distribution follow Gaussian and Inverse-Wishart distributions, multiple GMMs are generated by sampling from these distributions to assess epistemic uncertainty through classification variability. Aleatoric uncertainty is derived from the entropy of responsibility values within Gaussian components. Comparing our method with deep ensembles and logit-sampling for uncertainty computation demonstrates its superior performance in real-world applications for quantifying epistemic and aleatoric uncertainty, as well as detecting OOD samples. While deep ensembles miss some highly uncertain samples, our method successfully detects them and assigns high epistemic uncertainty.

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