  In the expanding landscape of AI-enabled robotics, robust quantification of
predictive uncertainties is of great importance. Three-dimensional (3D) object
detection, a critical robotics operation, has seen significant advancements;
however, the majority of current works focus only on accuracy and ignore
uncertainty quantification. Addressing this gap, our novel study integrates the
principles of conformal inference (CI) with information theoretic measures to
perform lightweight, Monte Carlo-free uncertainty estimation within a
multimodal framework. Through a multivariate Gaussian product of the latent
variables in a Variational Autoencoder (VAE), features from RGB camera and
LiDAR sensor data are fused to improve the prediction accuracy. Normalized
mutual information (NMI) is leveraged as a modulator for calibrating
uncertainty bounds derived from CI based on a weighted loss function. Our
simulation results show an inverse correlation between inherent predictive
uncertainty and NMI throughout the model's training. The framework demonstrates
comparable or better performance in KITTI 3D object detection benchmarks to
similar methods that are not uncertainty-aware, making it suitable for
real-time edge robotics.
