Remote camera helps robots navigate without prebuilt maps
ReVNM: Learning-Based Visual Navigation from a Remote Camera
Robotics
Summary
Robots usually need to see the world from their own eyes or use maps to move safely. This paper shows how a single camera placed away from the robot can act like both the robot’s eyes and a map. The researchers created a method that teaches the robot to imagine what it would see from its own viewpoint, even though the camera looks from far away. This allows the robot to find paths and avoid obstacles without carrying complex sensors or maps. Their tests show this approach works well in simulations and real robot tasks.
What this means in practice
- •For robotics engineers: Enable robots to navigate indoors using just one surveillance camera’s view without pre-existing maps or onboard cameras.
- •For building automation teams: Use remote cameras to guide cleaning or delivery robots safely through complex indoor environments.
Authors
Michikuni Eguchi, Kohei Honda, Masafumi Endo, Yasuhiro Yoshimura, Ryo Yonetani
Abstract
Visual Navigation Models (VNMs) enable robots to navigate from egocentric visual observations without geometric localization and planning, but long-range navigation still requires pre-built maps. This paper presents the Remote Visual Navigation Model (ReVNM), which uses a single remote surveillance camera to serve as both an observation source and an implicit environmental map for visual navigation. While the use of remote cameras could eliminate the need for pre-built maps as well as onboard vision processing, their limited field of view instead of egocentric observations makes it hard to achieve collision-free navigation. The lack of existing data with diverse remote viewpoints, which are crucial for training robust VNMs, further complicates the challenge. In this work, we propose a learning-by-synthesis approach to address this two-fold challenge. Our ReVNM extends a state-of-the-art VNM architecture with an exocentric-to-egocentric (exo2ego) module that predicts an egocentric depth observation from remote-camera observations. This helps the VNM to plan a path while considering obstacles in front of the robot. Trained only on randomly generated worlds with diverse obstacle layouts and camera viewpoints, ReVNM can generalize well to real robot navigation without additional fine-tuning. Experiments in both simulation and real-world environments confirmed the effectiveness of the proposed approach.