Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method using a GelSight tactile sensor and a depth camera. Local shape is recovered from tactile images via a learned model trained in simulation. Through efficient inference on a spatial factor graph informed by a Gaussian process, we build an implicit surface representation of the object. We demonstrate visuo-tactile mapping in both simulated and real-world experiments, to incrementally build 3-D reconstructions of household objects.

Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.

In this paper, we present an active SLAM framework for volumetric exploration of 3D underwater environments with multibeam sonar. Recent work in integrated SLAM and planning performs localization while maintaining volumetric free-space information. However, an absence of informative loop closures can lead to imperfect maps, and therefore unsafe behavior. To solve this, we propose a navigation policy that reduces vehicle pose uncertainty by balancing between volumetric exploration and revisitation. To identify locations to revisit, we build a 3D visual dictionary from real-world sonar data and compute a metric of submap saliency. Revisit actions are chosen based on propagated pose uncertainty and sensor information gain. Loop closures are integrated as constraints in our pose-graph SLAM formulation and these deform the global occupancy grid map. We evaluate our performance in simulation and real-world experiments, and highlight the advantages over an uncertainty-agnostic framework.

In this paper, we introduce an ambiguity-aware robust active SLAM (ARAS) framework that makes use of multi-hypothesis state and map estimations to achieve better robustness. Ambiguous measurements can result in multiple probable solutions in a multi-hypothesis SLAM (MH-SLAM) system if they are temporarily unsolvable (due to insufficient information), our ARAS aims at taking all these probable estimations into account explicitly for decision making and planning, which, to the best of our knowledge, has not yet been covered by any previous active SLAM approach (which mostly consider a single hypothesis at a time). This novel ARAS framework 1) adopts local contours for efficient multi-hypothesis exploration, 2) incorporates an active loop closing module that revisits mapped areas to acquire information for hypotheses pruning to maintain the overall computational efficiency, and 3) demonstrates how to use the output target pose for path planning under the multi-hypothesis estimations. Through extensive simulations and a real-world experiment, we demonstrate that the proposed ARAS algorithm can actively map general indoor environments more robustly than a similar single-hypothesis approach in the presence of ambiguities.

The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch's category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.