Preferential Multi-Target Search in Indoor Environments using Semantic SLAM

TL;DR

This paper introduces a semantic SLAM-based heuristic for service robots to efficiently locate multiple objects in indoor environments, enhancing multi-target search capabilities for complex tasks.

Contribution

It presents a novel heuristic integrated with semantic SLAM for preferential multi-target search, resilient to obstacles and adaptable to various detection and navigation methods.

Findings

Effective multi-target search demonstrated in real-world indoor environments.

Resilience to obstacles that block navigation goals.

Flexible integration with different object detectors and SLAM techniques.

Abstract

In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearch