Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as "passing a shop on the left" or "moving through a forested area". Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high-performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets - one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-look behaviours, and compare to existing state-of-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components.Comment: 9 pages, 11 figures, ICRA 201

LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can recognize where they are when travelling in the reverse direction along a route for the first time, without having to turn back and look. The difficulty of this problem exceeds any addressed in past appearance- and viewpoint-invariant visual place recognition (VPR) research, in part because large parts of the scene are not commonly observable from opposite directions. Consequently, as shown in this paper, the precision-recall performance of current state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders of magnitude below what would be usable in a closed-loop system. Current engineered solutions predominantly rely on panoramic camera or LIDAR sensing setups; an eminently suitable engineering solution but one that is clearly very different to how humans navigate, which also has implications for how naturally humans could interact and communicate with the navigation system. In this paper we develop a suite of novel semantic- and appearance-based techniques to enable for the first time high performance place recognition in this challenging scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of images using the convolutional feature maps from a state-of-the-art dense semantic segmentation network. Then, to verify the spatial semantic arrangement of the top matching candidates, we develop a novel approach for mining semantically-salient keypoint correspondences.Comment: Accepted for Robotics: Science and Systems (RSS) 2018. Source code now available at https://github.com/oravus/lost

Episode-based active learning with Bayesian neural networks

We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final training on the CIFAR-10 dataset shows that incremental network updates with final training on the accumulated acquisition set are essential for best performance, while limiting the amount of required human labeling labor

The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems

Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, gender, identity, activity and even emotional state of humans within that scene. This raises important questions about the reach, lifespan, and potential misuse of this information. This paper is a call to action to consider privacy in the context of robotic vision. We propose a specific form privacy preservation in which no images are captured or could be reconstructed by an attacker even with full remote access. We present a set of principles by which such systems can be designed, and through a case study in localisation demonstrate in simulation a specific implementation that delivers an important robotic capability in an inherently privacy-preserving manner. This is a first step, and we hope to inspire future works that expand the range of applications open to sighted robotic systems.Comment: 7 pages, 6 figure

Sondeo arqueológico Cueva Pintada corte 0 cierre sur [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Stereo odometry - A review of approaches (Technical Report 3/07)

Estimating its ego-motion is one of the most important capabilities for an autonomous mobile platform. Without reliable ego-motion estimation no long-term navigation is possible. Besides odometry, inertial sensors, DGPS, laser range finders and so on, vision based algorithms can contribute a lot of information. Stereo odometry is a vision based motion estimation algorithm that estimates the ego-motion of a stereo camera through its environment by evaluating the captured images. In this paper, we want to give an integrated overview of stereo odometry and the accompanying literature. We want to emphasize the fact that stereo odometry is a chain of several single subprocesses where each relies on its predecessor’s results. A variety of exchangable methods for each of these subprocesses is available. The key to a more accurate and efficient stereo odometry lies in an integrated analysis of its single subprocesses and the many algorithms available

A generic scheme for robust probabilistic estimation using graphical models

Probabilistic estimation using graphical models plays an important role in today’s intelligent and autonomous systems. This paper summarizes our work on robust probabilistic estimation using such models. This robustness, i.e. the algorithmic fault-tolerance in the presence of outliers is crucial for any autonomous system aiming at long-term operation. We show how probabilistic estimation using factor graphs can be made tolerant against outliers in the underlying data and demonstrate the feasibility of the proposed generic scheme in the domains of SLAM and satellite-based localization