Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 18 references
Abstract
For mobile robots to execute commands given by humans in environments lacking localization infrastructure, they must be capable of making semantic inferences about the environment. To this end, we present a task-based navigation method for a mobile robot using single monocular camera. The robot’s perception mechanism consists of a SSD-InceptionV2 for target object detection and MiDaS-Small for depth map estimation. By exploiting the geometric advantage of the motion plane of ground robots, metric distances derived from relative depth outputs are used in the obstacle avoidance algorithm, and a Kalman filter is applied to ensure continuity in target object tracking. Tests conducted on a Jetson TX2 platform verified that the system exhibits stable performance at approximately 15 FPS and successfully completes its task. The results demonstrate that mobile robot navigation systems can be developed in infrastructure-free environments using solely camera-based perception.
Results show that search efficiency depends on alignment between exploration design and spatial structure, not universal strategy superiority, and that search efficiency depends on alignment between exploration design and spatial structure, not universal strategy superiority.
Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
T. Q. Le, T. Luu· IAES International Journal o...· 0 citations
Robotic platforms operating in GPS-denied environments require robust ego-motion estimation systems that fuse complementary sensor modalities under onboard computational constraints. This paper proposes a navigation framework estimating six-degree-of-freedom (6 DoF) robot pose in unstructured scenes using a monocular c...
Pratik Dhameliya· 2026 6th International Confe...· 0 citations
This work presents a data-efficient, interpretable method for vision-based dynamic obstacle avoidance that operates entirely on real-world data, avoiding the sim-to-real transfer problem inherent in simulation-trained policies.
Erik Jagnandan, M. Haile, Gregory Barber et al.· arXiv.org· 0 citations
The results demonstrate that integrating vision-based semantic data with geometric constraints significantly improves collision avoidance and success rates in cluttered, multi-room scenarios in cluttered, multi-room scenarios.
Giovanbattista Gravina, Francesco D'Orazio, Michele Cipriano et al.· Frontiers in Robotics and AI· 0 citations
This work introduces online detection-driven goal re-finement, which extracts target crops from the robot’s live camera stream and progressively updates the goal representation as the robot approaches the target, reducing dependence on pre-collected goal images and improving robustness to distant or low-quality detectio...
