How Can Uncertainty Make Robot Navigation Safer?

Robots moving inside places like restaurants or hospitals need to know exactly where they are. A new study proposes a method that uses uncertainty to boost localization accuracy without altering the prediction model. The system assesses two uncertainty types: aleatoric, caused by data noise, and epistemic, due to limited model knowledge. Then, using a percentile threshold, it discards pose predictions (x, y, yaw) that are too uncertain. Applied to a network fusing RGB images and 2D LiDAR data, the approach was tested on three real datasets collected by a service robot. With stricter thresholds (90 %, 80 %, 70 %), average position errors dropped by 41 %, 56 % and 69 %, while orientation errors fell by 56 %, 66 % and 73 %. The strategy also removes extreme outliers, aligning estimated trajectories better with the ground truth. This is the first quantitative proof that a simple uncertainty‑based rejection rule can make multimodal, end‑to‑end localization systems more reliable, paving the way for safer robots in homes and public spaces.

The proposed method separates aleatoric and epistemic uncertainty components. Aleatoric uncertainty is modeled via a probability distribution predicted by the network, typically a variance for each pose output. Epistemic uncertainty is obtained using Monte‑Carlo dropout or ensemble techniques, providing a variance measure across multiple forward passes of the model. The percentile threshold (90 %, 80 %, 70 %) is computed on the aggregated uncertainty values, ranking predictions and discarding the highest ones. This approach excels in multimodal scenarios where RGB and LiDAR data offer complementary information: images capture texture and color, while LiDAR provides precise geometric measurements. Fusion occurs in an end‑to‑end network that learns a shared representation, reducing reliance on pre‑processing pipelines. Experimental results show that, beyond lowering mean errors, the rejection strategy reduces estimate variance, enhancing robustness against adverse environmental conditions (variable lighting, moving obstacles). Practical applications include delivery robots, domestic assistants, or indoor autonomous vehicles, where safety demands reliable localization even with noisy data or unseen training scenarios.