This paper addresses the problem of robotic exploration of unknown indoor environments with deadlines. Indoor exploration using mobile robots has typically focused on exploring the entire environment without considering deadlines. The objective of the prioritized exploration in this paper is to rapidly compute the geometric layout of an initially unknown environment by exploring key regions of the environment and returning to the home location within a deadline. This prioritized exploration is useful for time-critical and dangerous environments where rapid robot exploration can provide vital information for subsequent operations. For example, firefighters, for whom time is of the essence, can utilize the map generated by this robotic exploration to navigate a building on fire. In our previous work, we showed that a priority-based greedy algorithm can outperform a cost-based greedy algorithm for exploration under deadlines. This paper models the prioritized exploration problem as an Orienteering Problem (OP) and a Profitable Tour Problem (PTP) in an attempt to generate exploration strategies that can explore a greater percentage of the environment in a given amount of time. The paper presents simulation results on multiple graph-based and Gazebo environments. We found that in many cases the priority-based greedy algorithm performs on par or better than the OP and PTP-based algorithms. We analyze the potential reasons for this counterintuitive result.

We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.

Indoor exploration using mobile robots has typically focused on exploring the entire environment without considering deadlines. This paper introduces a priority-based exploration algorithm for situations with an initially unknown and dynamically assigned deadline. The goal of our exploration strategy is to determine the geometric structure of an unknown environment as rapidly as possible and return to the home location. This is necessary for dangerous environments where an initial rapid robot exploration provides critical information about the layout for subsequent operations. For example, firefighters, for whom time is of the essence, can utilize the map generated by this robotic exploration to navigate a building on fire. We present a three-part strategy to solve this problem. First, we represent the physical environment as an exploration graph, whose vertices represent the local environment with its geometric and semantic information. Second, we assign priority values to these vertices based on their environment regions. Third, we present a graph exploration algorithm that employs the vertex priorities. Simulation experiments on a set of graph environments and Gazebo environments demonstrate that, in contrast to prior approaches that ignore the semantic information, our priority-based exploration algorithm enables the robot to efficiently explore more of the environment while satisfying its deadline constraints.

This paper considers the problem of finding pose matches between trajectories of multiple robots in their respective coordinate frames or equivalent matches between trajectories obtained during different sessions. Pose correspondences between trajectories are mediated by common landmarks represented in a topological map lacking distinct metric coordinates. Despite such lack of explicit metric level associations, we mine preliminary pose level correspondences between trajectories through a novel multi-scale heat-kernel descriptor and correspondence graph framework. These serve as an improved initialization for ICP (Iterative Closest Point) to yield dense pose correspondences. We perform extensive analysis of the proposed method under varying levels of pose and landmark noise and showcase its superiority in obtaining pose matches in comparison with standard ICP like methods. To the best of our knowledge, this is the first work of the kind that brings in elements from spectral graph theory to solve the problem of pose correspondences in a multi-robotic setting and differentiates itself from other works.