This article proposes a novel indoor magnetic field-based place recognition algorithm that is accurate and fast to compute. For that, we modified the generalized ''Hough Transform'' to process magnetic data (MagHT). It takes as input a sequence of magnetic measures whose relative positions are recovered by an odometry system and recognizes the places in the magnetic map where they were acquired. It also returns the global transformation from the coordinate frame of the input magnetic data to the magnetic map reference frame. Experimental results on several real datasets in large indoor environments demonstrate that the obtained localization error, recall, and precision are similar to or are better than state-of-the-art methods while improving the runtime by several orders of magnitude. Moreover, unlike magnetic sequence matching-based solutions such as DTW, our approach is independent of the path taken during the magnetic map creation.
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset. In this paper, we observe the performance of two of the latest state-of-the-art trajectory prediction methods across four different datasets (Argoverse, NuScenes, Interaction, Shifts). This analysis allows to gain some insights on the generalizability proprieties of most recent trajectory prediction models and to analyze which dataset is more representative of real driving scenes and therefore enables better transferability. Furthermore we present a novel method to estimate prediction uncertainty and show how it could be used to achieve better performance across datasets.
: Sample-efficiency is still a major challenge for reinforcement-learning (RL) algorithms, particularly when learning directly from image inputs. We propose a simple two-step pipeline: First, learn a visual representation of the scene by pre-training an encoder from multiple supervised computer-vision objectives, then train an RL agent which can focus solely on solving the task. We evaluate our method on 3 realistic manipulation tasks with a simulated 6-degrees-of-freedom robot. We show that not only is our method much more sample-efficient than an end-to-end baseline, but it also reaches a higher final success rate, even solving one of the tasks where the baseline fails to make any progress. Additionally, by adding domain randomization techniques into our pipeline, we are able to solve a simpler reaching task consistently in the real world via zero-shot sim-to-real transfer.
