Over the past few years, we have witnessed a considerable diffusion of data-driven visual odometry (VO) approaches as viable alternatives to standard geometric-based strategies. Their success is mainly related to the improved robustness to image nonideal conditions (e.g., blur, high or low contrast, texture-poor scenarios). However, most of the data-driven State-of-the-Art (SotA) approaches do not provide any kind of information about the uncertainty of their estimates, which is crucial to effectively integrate them into robotic navigation systems. Inspired by this considerations, we propose uncertainty-aware VO (UA-VO), a novel deep neural network (DNN) architecture that computes relative pose predictions by processing sequence of images and, at the same time, provides uncertainty measures about those estimations. The confidence measure computed by UA-VO considers both epistemic and aleatoric uncertainties and accounts for heteroscedasticity, i.e., it is sample-dependent. We assess the benefits of UA-VO with different typology of experiments on three publicly available datasets and on a brand new set of sequences, we gathered to extend the evaluation.
Uncertainty Estimation for Data-Driven Visual Odometry
Costante, Gabriele
;Mancini, Michele
2020
Abstract
Over the past few years, we have witnessed a considerable diffusion of data-driven visual odometry (VO) approaches as viable alternatives to standard geometric-based strategies. Their success is mainly related to the improved robustness to image nonideal conditions (e.g., blur, high or low contrast, texture-poor scenarios). However, most of the data-driven State-of-the-Art (SotA) approaches do not provide any kind of information about the uncertainty of their estimates, which is crucial to effectively integrate them into robotic navigation systems. Inspired by this considerations, we propose uncertainty-aware VO (UA-VO), a novel deep neural network (DNN) architecture that computes relative pose predictions by processing sequence of images and, at the same time, provides uncertainty measures about those estimations. The confidence measure computed by UA-VO considers both epistemic and aleatoric uncertainties and accounts for heteroscedasticity, i.e., it is sample-dependent. We assess the benefits of UA-VO with different typology of experiments on three publicly available datasets and on a brand new set of sequences, we gathered to extend the evaluation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
