Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements (1805.12338v2)

Abstract: Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance to obstacles from richer sensor readings such as 3D lasers or RGBD sensors, we present a method to estimate the distance directly from raw 2D laser data. To learn a mapping from raw 2D laser distances to obstacle distances we frame the problem as a learning task and train a neural network formed as an autoencoder. A novel configuration of network hyperparameters is proposed for the task at hand and is quantitatively validated on a test set. Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the trained network can successfully infer obstacle distances from partial 2D laser readings.

• The paper introduces a neural autoencoder with skip connections and γ-scaling to accurately infer obstacle distances from partial laser data.
• The methodology synthesizes ground truth by merging laser scans with depth images, effectively reducing RMSLE errors in distance prediction.
• Experimental results, including tests on a Care-O-bot 4, validate the approach’s improvement in detecting obstacles in challenging environments.

Inferring Obstacle Distances with Hallucinating Robots

The paper, "Hallucinating Robots: Inferring Obstacle Distances from Partial Laser Measurements" by Jens Lundell, Francesco Verdoja, and Ville Kyrki, addresses a pertinent issue in mobile robotics: the difficulty of detecting and accurately determining the proximity of obstacles using 2D laser sensors. These sensors, while popular due to their fast data acquisition and large angular fields, often struggle with transparent objects or complex shapes that are not fully visible at their scanning height. This paper introduces a novel method for approximating robot-to-obstacle distances using only 2D laser data, bypassing the need for richer but computationally heavier sensors like 3D lasers or RGBD cameras.

Methodology and Contributions

The core of the paper is the framing of the obstacle distance estimation as a learning task. A neural network structured as an autoencoder is employed to infer the true distances from partial 2D laser readings. The authors introduce a unique configuration of network hyperparameters, including the use of convolutional layers with skip connections to enhance detailed information transfer and the integration of non-uniform output scaling (γ-scaling) to improve resolution for closer distances, which are critical for navigation safety. The model's output is validated through a series of quantitative tests, with performance gains noted particularly in the presence of skip connections and the tailored architecture.

A significant challenge tackled in the paper is generating the ground truth for supervised learning. The team developed a method to synthesize this data by combining partial laser scans with overlapping depth images, allowing for a more accurate representation of the environment.

Experimental Evaluation

Performance was evaluated using both quantitative and qualitative metrics. For the quantitative analysis, various configurations of the proposed network were trained and tested across datasets collected in different environments, noting the capability of the model to consistently reduce RMSLE in predicting true distances. Qualitatively, real-time implementation on a Care-O-bot 4 illustrated practical feasibility, with the trained network effectively predicting obstacle distances in environments featuring complex obstacles, such as glass-walled rooms and rearranged furnishings.

Implications and Future Directions

This research demonstrates a practical approach to enhance the obstacle detection capabilities of mobile robots using existing hardware, potentially reducing computational demand and improving robot safety without sensor upgrades. The framework may serve as a foundational model for further innovations in robotic perception, specifically in environments where sensor fusion or richer data inputs are not feasible.

Future research could focus on several promising pathways posited in this work, such as augmenting the training data with synthetic objects to improve generalization across diverse settings, integrating temporal aspects for handling dynamic obstacles, and merging this approach with end-to-end navigation systems to realize more comprehensive, reliable autonomous systems.

In conclusion, the paper presents a methodologically sound and practically relevant contribution to mobile robotics, emphasizing the utility of neural networks in bridging sensor limitations to enhance environmental awareness and navigation safety. The techniques and findings outlined may influence subsequent research and development in mobile perception systems, informing the design of robust, adaptable solutions tailored to operate effectively in varied and challenging environments.