arXiv subjects

Jeff Pflueger

Publications and source records attributed to Jeff Pflueger.

2 recordsLinked to original sources

arXiv2025

Approximating Safety Feedback Without a Safety Oracle via Model Predictive Control

Safe decision-making algorithms for control of mobile robots often require the existence of feedback to verify the safety of proposed actions. This feedback is assumed to be directly available during the development or deployment of the control system. It can take the form of either an explicit constraint formulation or a set of hand-labeled safety data, both of which can be inaccurate or time consuming to produce. Many recently developed simulators can handle complex interactions and varied environments. These environments have implicit safety constraints that may be hard to model. By leveraging one of these simulators, we can construct a proxy for a safety function that bypasses the need for hand designed feedback in capturing these constraints. We present an algorithm that approximates safety by using reversibility and a positive-invariance assumption on the unsafe state space. This method employs the Model-Predictive Path Integral algorithm (MPPI) to establish this reversibility and verify a proposed action. First the action is projected via the simulator to a future state. Then if MPPI can find a path back to a previous state in the trajectory, that state is guaranteed to be outside the unsafe (positive invariant) set. Experimental results demonstrate that the proposed algorithm can approximate the performance of a safety oracle while avoiding classification of unsafe states as safe.

cs.LG↗

arXiv2024

LiDAR Inertial Odometry And Mapping Using Learned Registration-Relevant Features

SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that either operate directly the full pointclouds or on extracted features face key tradeoffs in accuracy and computational efficiency (e.g., memory consumption). To address these issues, this paper presents DFLIOM with several key innovations. Unlike previous methods that rely on handcrafted heuristics and hand-tuned parameters for feature extraction, we propose a learning-based approach that select points relevant to LiDAR SLAM pointcloud registration. Furthermore, we extend our prior work DLIOM with the learned feature extractor and observe our method enables similar or even better localization performance using only about 20\% of the points in the dense point clouds. We demonstrate that DFLIOM performs well on multiple public benchmarks, achieving a 2.4\% decrease in localization error and 57.5\% decrease in memory usage compared to state-of-the-art methods (DLIOM). Although extracting features with the proposed network requires extra time, it is offset by the faster processing time downstream, thus maintaining real-time performance using 20Hz LiDAR on our hardware setup. The effectiveness of our learning-based feature extraction module is further demonstrated through comparison with several handcrafted feature extractors.

cs.RO↗