4 accepted papers
Although haptic sensing has recently been used for legged robot localization in extreme environments where a camera or LiDAR might fail, the problem of efficiently representing the haptic signatures in a learned prior map is still open. This paper introduces an approach to terrain representation for…
This paper demonstrates how an efficient repre-sentation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up both the inference and training of a DNN-based motion planner. We build upon our recent work on learning lo…
We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context acquired from synthetic LiDAR intensity images. T…
We describe and experimentally demonstrate a practical method for indoor localization using measurements obtained from resource-constrained devices with limited sensing capabilities. We focus on handheld/mobile devices but the method can be useful for a variety of wearable devices. Our system works…