Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimation

Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single horizontal slice of the environment, missing critical obstacles above or below the scan plane. We present a teacher-student framework for vision-based mobile robot navigation that eliminates the need for LiDAR sensors. A teacher policy trained via Proximal Policy Optimization (PPO) in NVIDIA Isaac Lab leverages privileged 2D LiDAR observations that account for the full robot footprint to learn robust navigation. The learned behavior is distilled into a student policy that relies solely on monocular depth maps predicted by a fine-tuned Depth Anything V2 model from four RGB cameras. The complete inference pipeline, comprising monocular depth estimation (MDE), policy execution, and motor control, runs entirely onboard an NVIDIA Jetson Orin AGX mounted on a DJI RoboMaster platform, requiring no external computation for inference. In simulation, the student achieves success rates of 82-96.5%, consistently outperforming the standard 2D LiDAR teacher (50-89%). In real-world experiments, the MDE-based student outperforms the 2D LiDAR teacher when navigating around obstacles with complex 3D geometries, such as overhanging structures and low-profile objects, that fall outside the single scan plane of a 2D LiDAR.

Accuracy evaluation of a Low-Cost Differential Global Positioning System for mobile robotics

Differential GPS, commonly referred as DGPS, is a well-known and very accurate localization system for many outdoor applications in particular for mobile outdoor robotics. The most common drawback of DGPS systems are the high costs for both base station and receivers. In this paper, we present a setup that uses third-party open-source software and a Ublox ZED-F9P chip to build a ROS-enabled low-cost DGPS setup that is ready to use in a few hours. The main goal of this paper is to analyze and evaluate the repetitive and absolute accuracy of the system. The first measurement also examines the differences between a SAPOS base station and a locally installed one consisting of low-cost components. During the evaluation process of the absolute accuracy, a moving mobile robot is used on the receiver side. It is tracked through a highly accurate VICON motion capture system.