Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére

Agent and object aware tracking and mapping methods for mobile manipulators

The age of the intelligent machine is upon us. They exist in our factories, our warehouses, our military, our hospitals, on our roads, and on the moon. Most of these things we call robots. When placed in a controlled or known environment such as an automotive factory or a distribution warehouse they perform their given roles with exceptional efficiency, achieving far more than is within reach of a humble human being. Despite the remarkable success of intelligent machines in such domains, they have yet to make a full-hearted deployment into our homes. The missing link between the robots we have now and the robots that are soon to come to our houses is perception. Perception as we mean it here refers to a level of understanding beyond the collection and aggregation of sensory data. Much of the available sensory information is noisy and unreliable, our homes contain many reflective surfaces, repeating textures on large flat surfaces, and many disruptive moving elements, including humans. These environments change over time, with objects frequently moving within and between rooms. This idea of change in an environment is fundamental to robotic applications, as in most cases we expect them to be effectors of such change. We can identify two particular challenges1 that must be solved for robots to make the jump to less structured environments - how to manage noise and disruptive elements in observational data, and how to understand the world as a set of changeable elements (objects) which move over time within a wider environment. In this thesis we look at one possible approach to solving each of these problems. For the first challenge we use proprioception aboard a robot with an articulated arm to handle difficult and unreliable visual data caused both by the robot and the environment. We use sensor data aboard the robot to improve the pose tracking of a visual system when the robot moves rapidly, with high jerk, or when observing a scene with little visual variation. For the second challenge, we build a model of the world on the level of rigid objects, and relocalise them both as they change location between different sequences and as they move. We use semantics, image keypoints, and 3D geometry to register and align objects between sequences, showing how their position has moved between disparate observations.Open Acces

GOAT: GO to Any Thing

In deployment scenarios such as homes and warehouses, mobile robots are expected to autonomously navigate for extended periods, seamlessly executing tasks articulated in terms that are intuitively understandable by human operators. We present GO To Any Thing (GOAT), a universal navigation system capable of tackling these requirements with three key features: a) Multimodal: it can tackle goals specified via category labels, target images, and language descriptions, b) Lifelong: it benefits from its past experience in the same environment, and c) Platform Agnostic: it can be quickly deployed on robots with different embodiments. GOAT is made possible through a modular system design and a continually augmented instance-aware semantic memory that keeps track of the appearance of objects from different viewpoints in addition to category-level semantics. This enables GOAT to distinguish between different instances of the same category to enable navigation to targets specified by images and language descriptions. In experimental comparisons spanning over 90 hours in 9 different homes consisting of 675 goals selected across 200+ different object instances, we find GOAT achieves an overall success rate of 83%, surpassing previous methods and ablations by 32% (absolute improvement). GOAT improves with experience in the environment, from a 60% success rate at the first goal to a 90% success after exploration. In addition, we demonstrate that GOAT can readily be applied to downstream tasks such as pick and place and social navigation

A Survey on Global LiDAR Localization

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR scanners have become a standard sensor for robot localization and mapping. This article surveys recent progress and advances in LiDAR-based global localization. We start with the problem formulation and explore the application scope. We then present the methodology review covering various global localization topics, such as maps, descriptor extraction, and consistency checks. The contents are organized under three themes. The first is the combination of global place retrieval and local pose estimation. Then the second theme is upgrading single-shot measurement to sequential ones for sequential global localization. The third theme is extending single-robot global localization to cross-robot localization on multi-robot systems. We end this survey with a discussion of open challenges and promising directions on global lidar localization