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Soft Robotics Workshop at IROS 2020

Perla Maiolino of Soft Robotics Lab will be delivering a workshop for IROS 2020 entitled "Application-Driven soft Robotic Systems: Translational Challenges"   (For more information click here).

Soft Robotics Workshop at IROS 20202020-06-02T15:28:09+01:00

Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors

In this paper we consider long-term navigation using fixed 2D LIDARs. We consider how localization algorithms based on scan-matching - commonly used in indoor environments - are prone to failure when exposed to a challenging real-world outdoor environment. The driving motivation behind this work is to produce a simple, robust system that can be utilized repeatedly over a long period, rather than [...]

Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors2016-10-22T19:49:35+01:00

Non-parametric Learning for Natural Plan Generation

We present a novel way to learn sampling distributions for sampling-based motion planners by making use of expert data. We learn an estimate (in a non-parametric setting) of sample densities around semantic regions of interest, and incorporate these learned distributions into a sampling-based planner to produce natural plans. Our motivation is that certain aspects of [...]

Non-parametric Learning for Natural Plan Generation2016-10-22T19:51:02+01:00

Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification

Abstract— This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multi-class Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the [...]

Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification2016-10-22T19:51:02+01:00