The number and arrangement of sensors on mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates.

OASIS: Optimal Arrangements for Sensing in SLAM

MAVREN: A multilayered adaptive framework for deploying vision-language models on resource-constrained unmanned aerial vehicles for autonomous search and rescue

: With the adoption of autonomous driving systems and scenario-based testing, there is a growing need for efficient methods to understand and retrieve driving scenarios from vast amounts of real-world driving data. As manual scenario selection is labor-intensive and limited in scalability, this study explores the use of three Large Vision-Language Models, CLIP, BLIP-2, and BakLLaVA, for scenario retrieval. The ability of the models to retrieve relevant scenarios based on natural language queries is evaluated using a diverse benchmark dataset of real-world driving scenarios and a precision metric. Factors such as scene complexity, weather conditions, and different traffic situations are incorporated into the method through the 6-Layer Model to measure the effectiveness of the models across different driving contexts. This study contributes to the understanding of the capabilities and limitations of Large Vision-Language Models in the context of driving scenario retrieval and provides implications for future research directions.

Towards Scenario Retrieval of Real Driving Data with Large Vision-Language Models

Automated driving functions make mobility safer and more comfortable. The perception of these functions relies on sensors that capture the vehicle’s environment. A sound configuration of the sensor’s intrinsic and extrinsic parameters is crucial for a high perception performance. Simulation is a method of increasing importance for the evaluation and optimization of sensor sets since it enables fast and cost-effective procedures. This paper presents a method for evaluating and optimizing cameras during traffic scenarios in a simulation based on their raw sensor data quality. We present a framework using the CARLA simulator and evaluate the coverage and visibility of the surrounding objects during virtual traffic scenarios. For this purpose, we propose a general metric to measure the acquired information content of camera systems. We compare our metric to the performance of a state-of the-art object detector to investigate the ability to represent detection performance. The suitability of the metric for the optimization of intrinsic hardware parameters and extrinsic mounting parameters is explored. We show in our experiments that the metric correlates strongly with a benchmark perception function’s performance. Furthermore, we prove that sensor configurations optimized by our metric outperform a baseline sensor set in terms of object detection performance.

A Method for Evaluation and Optimization of Automotive Camera Systems based on Simulated Raw Sensor Data

What the Cluster: An Unsupervised Framework to Segment Automotive Doppler Radar Pointclouds

: Radar sensors rank among the most common sensors used for highly automated driving functions due to their solid distance and velocity measurement capabilities and their robustness against adversarial environmental conditions. However, radar point clouds are noisy and must therefore be filtered. This work reviews current research with the aim to make radar detections usable for safe perception functions which require a guarantee for correctness of the measured environmental representation. The impact on radar errors on the distinct downstream tasks is explained. Besides, the term of safety for automated driving functions is illuminated under consideration of the current standards and state-of-the-art research interpreting these standards is presented. Furthermore, this work discusses safe radar signal processing and filtering, approaches to enrich radar data points by information fusion, e.g. from cameras and other radars, and development tools for safe radar-based perception functions. Finally, next steps on the way towards safety guarantees for radar sensors are identified.

Making Radar Detections Safe for Autonomous Driving: A Review

A Taxonomy and Survey on Sensor Blockage-Induced Degradation and its Impact on Advanced Driver Assistance Systems

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