Learning Kalman network : a deep monocular visual odometry for on-road driving

This paper proposes a Learning Kalman Network (LKN) based monocular visual odometry (VO), i.e. LKN-VO, for on-road driving. Most existing learning-based VO focus on ego-motion estimation by comparing the two most recent consecutive frames. By contrast, the LKN-VO incorporates a learning ego-motion estimation through the current measurement, and a discriminative state estimator through a sequence of previous measurements. Superior to the model-based monocular VO, a more accurate absolute scale can be learned by LKN without any geometric constraints. In contrast to the model-based Kalman Filter (KF), the optimal model parameters of LKN can be obtained from dynamic and deterministic outputs of the neural network without elaborate human design. LKN is a hybrid approach where we achieve the non-linearity of the observation model and the transition model though deep neural networks, and update the state following the Kalman probabilistic mechanism. In contrast to the learning-based state estimator, a sparse representation is further proposed to learn the correlations within the states from the car’s movement behaviour, thereby applying better filtering on the 6DOF trajectory for on-road driving. The experimental results show that the proposed LKN-VO outperforms both model-based and learning state-estimator-based monocular VO on the most well-cited on-road driving datasets, i.e. KITTI and Apolloscape. In addition, LKN-VO is integrated with dense 3D mapping, which can be deployed for simultaneous localization and mapping in urban environments

A Multi-Center Study of Neurofilament Assay Reliability and Inter-Laboratory Variability

Objectives: Significantly elevated levels of neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH) have been described in the blood and cerebrospinal fluid (CSF) of amyotrophic lateral sclerosis (ALS) patients. The aim of this study was to evaluate the analytical performance of different neurofilament assays in a round robin with 10 centers across Europe/U.S. Methods: Serum, plasma and CSF samples from a group of five ALS and five neurological control patients were distributed across 10 international specialist neurochemical laboratories for analysis by a range of commercial and in-house neurofilament assays. The performance of all assays was evaluated for their ability to differentiate between the groups. The inter-assay coefficient of variation was calculated where appropriate from sample measurements performed across multiple laboratories using the same assay. Results: All assays could differentiate ALS patients from controls in CSF. Inter-assay coefficient of variation of analytical platforms performed across multiple laboratories varied between 6.5% and 41.9%. Conclusions: This study is encouraging for the growing momentum toward integration of neurofilament measurement into the specialized ALS clinic. It demonstrates the importance of 'round robin' studies necessary to ensure the analytical quality required for translation to the routine clinical setting. A standardized neurofilament probe is needed which can be used as international benchmark for analytical performance in ALS.status: Published onlin