This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.

The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

Reliable detection of previously unseen classes under distributional shift remains a central challenge in concept learning and explainable artificial intelligence. In particular, high-performance deep learning models often lack statistically grounded mechanisms to signal when an instance deviates from learned concepts. This paper addresses this limitation by investigating whether conformal prediction can be effectively combined with a YOLOv5 deep learning classifier to enable principled counterfactual detection without prior exposure to the counterfactual class. As a controlled testbed, we employ Kandinsky patterns, a structured benchmark widely used in explainable AI research due to its rule-based generative transparency and suitability for concept learning studies. The proposed framework first classifies valid and invalid patterns and subsequently applies inductive conformal prediction to obtain calibrated prediction sets at a user-defined significance level. Counterfactual instances are, at start, identified based solely on information from known true and false patterns, without explicit training examples of the counterfactual class. Experimental results demonstrate that the conformalized detector reliably identifies a substantial proportion of previously unseen counterfactual patterns while maintaining statistical validity. In addition, the method flags unlabeled (“empty”) instances, thereby providing a principled signal for the emergence of new concepts. By conformalizing YOLOv5 outputs, the approach establishes a statistically sound mechanism for uncertainty-aware detection of divergent classes, contributing to robust and explainable concept learning in structured visual pattern recognition.