Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving localization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.

We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the robot does not have a model of what the scientist deems to be relevant visual observations. Thus, the robot can neither visually search for particular types of objects, nor focus its attention on parts of the scene that might be more relevant than the pre-specified waypoints and viewpoints. In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection. We propose and evaluate a weakly supervised video representation learning method that outperforms ImageNet embeddings for similarity tasks in the underwater domain. We also demonstrate the deployment of this similarity operator during informed visual navigation in collaborative environmental monitoring scenarios, in large-scale field trials, where the robot and a human scientist collaboratively search for relevant visual content. Code: https://github.com/rvl-lab-utoronto/visual_search_in_the_wild.

In this paper we consider inter-robot communication in the context of joint activities. In particular, we focus on convoying and passive communication for radio-denied environments by using whole-body gestures to provide cues regarding future actions. We develop a communication protocol whereby information described by codewords is transmitted by a series of actions executed by a swimming robot. These action sequences are chosen to optimize robustness and transmission duration given the observability, natural activity of the robot and the frequency of different messages. Our approach uses a convolutional network to make core observations of the pose of the robot being tracked, which is sending messages. The observer robot then uses an adaptation of classical decoding methods to infer a message that is being transmitted. The system is trained and validated using simulated data, tested in the pool and is targeted for deployment in the open ocean. Our decoder achieves.94 precision and.66 recall on real footage of robot gesture execution recorded in a swimming pool.