Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments

Abstract

This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment as well as simulated missions over a 10m×10m coral reef using real data show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model. I. INTRODUCTIONThe challenges of exploration in remote and extreme environments such as the deep seas [1], [2], cave systems [3], outer space [4] and during or after a natural disaster [5], [6] have much in common. It is expensive and inherently dangerous for humans to explore such locations directly; hence, the use of mobile robots is desirable. However, if communication bottlenecks exist in the environment, prohibiting live streaming of video or other sensor data, then direct control of the robots is generally not possible. This paper describes a novel approach to co-robotic exploration in communication starved environments, and presents a system implementation of an under-sea exploration robot for corobotic exploration of marine environments.Although physically controlling a robot can be achieved over relatively low bandwidth, it is difficult to transmit the scene information necessary for an operator or scientist to make high level navigational decisions. We propose a spatially correlated Chinese Restaurant Process (CRP)-based [7] scene understanding model, that can be tuned to operate with

Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments

“…The main application we explored in [42] was semantic mapping, a form of mission summarization. This work sought to address Problem 1 described in Section 1.4.1 by giving scientists greater insight into the mission status and what the robot had found.…”

“…The robot localization system used was built by Levi Cai using AprilTags [74]. This figure appeared in [42].…”

“…While the BNP-ROST hyperparameters , , have clearly observable qualitative effects on the resulting semantic maps (as seen in Figures 3-2 In [42], the hyperparameters , , were selected after running a gridsearch over 125 sets of values, which took several hours. This was slow and inefficient, and there were no guarantees that the 5 values tested per variable were close to the optimal values.…”

“…Figure A-3: Here we show the WARPLab AUV "Red", a modified BlueRobotics BlueROV, exploring an artificially created underwater environment at WHOI. Appeared in [42].…”

“…As described in [42], in this model we assign a word to one of the occupied "tables" in a Chinese restaurant at time with probability proportional to ( ( , ,…”