5 accepted papers
With the Autonomous Vehicle (AV) industry shifting towards machine-learned approaches for motion plan-ning [1], the performance of self-driving systems is starting to rely heavily on large quantities of expert driving demon-strations. However, collecting this demonstration data typically involves ex…
Precise localization of robots is imperative for their safe and autonomous navigation in both indoor and outdoor environments. In outdoor scenarios, the environment typically undergoes significant perceptual changes and requires robust methods for accurate localization. Monocular camera-based approa…
Visual place recognition under difficult perceptual conditions remains a challenging problem due to changing weather conditions, illumination and seasons. Long-term visual navigation approaches for robot localization should be robust to these dynamics of the environment. Existing methods typically l…
The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent appro…
In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix…