Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety challenges due to environmental uncertainties, sensor noise, and system-level interactions. This paper presents an integrated approach combining vision-based sensor fusion with System-Theoretic Process Analysis (STPA) to enhance the safety and robustness of VTOL UAV operations during take-off and landing. By incorporating fiducial markers, such as AprilTags, into the control architecture, and performing comprehensive hazard analysis, we identify unsafe control actions and propose mitigation strategies. Key contributions include developing the control structure with vision system capable of identifying a fiducial marker, multirotor controller and corresponding unsafe control actions and mitigation strategies. The proposed solution is expected to improve the reliability and safety of VTOL UAV operations, paving the way for resilient autonomous systems.

CRISP: Object Pose and Shape Estimation with Test-Time Adaptation

We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction and a DPT-based network for estimating pose-normalized points for pose estimation. As a second contribution, we propose an optimization-based pose and shape corrector that can correct estimation errors caused by a domain gap. Observing that the shape decoder is well behaved in the convex hull of known shapes, we approximate the shape decoder with an active shape model, and show that this reduces the shape correction problem to a constrained linear least squares problem, which can be solved efficiently by an interior point algorithm. Third, we introduce a self-training pipeline to perform self-supervised domain adaptation of CRISP. The self-training is based on a correct-and-certify approach, which leverages the corrector to generate pseudo-labels at test time, and uses them to self-train CRISP. We demonstrate CRISP (and the self-training) on YCBV, SPE3R, and NOCS datasets. CRISP shows high performance on all the datasets. Moreover, our self-training is capable of bridging a large domain gap. Finally, CRISP also shows an ability to generalize to unseen objects. Code and pre-trained models will be available on https://web.mit.edu/sparklab/research/crisp_object_pose_shape/.

Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation

Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises the Sim2Real domain gap problem. A major cause of the Sim2Real gap are novel lighting conditions encountered during test time. Event sensors have been shown to provide some robustness against lighting variations in vision-based pose estimation. However, challenging lighting conditions due to strong directional light can still cause undesirable effects in the output of commercial off-the-shelf event sensors, such as noisy/spurious events and inhomogeneous event densities on the object. Such effects are non-trivial to simulate in software, thus leading to Sim2Real gap in the event domain. To close the Sim2Real gap in event-based satellite pose estimation, the paper proposes a test-time self-supervision scheme with a certifier module. Self-supervision is enabled by an optimisation routine that aligns a dense point cloud of the predicted satellite pose with the event data to attempt to rectify the inaccurately estimated pose. The certifier attempts to verify the corrected pose, and only certified test-time inputs are backpropagated via implicit differentiation to refine the predicted landmarks, thus improving the pose estimates and closing the Sim2Real gap. Results show that the our method outperforms established test-time adaptation schemes.

A Certifiable Algorithm for Simultaneous Shape Estimation and Object Tracking

Applications from manipulation to autonomous vehicles rely on robust and general object tracking to safely perform tasks in dynamic environments. We propose the first certifiably optimal category-level approach for simultaneous shape estimation and pose tracking of an object of known category (e.g. a car). Our approach uses 3D semantic keypoint measurements extracted from an RGB-D image sequence, and phrases the estimation as a fixed-lag smoothing problem. Temporal constraints enforce the object's rigidity (fixed shape) and smooth motion according to a constant-twist motion model. The solutions to this problem are the estimates of the object's state (poses, velocities) and shape (paramaterized according to the active shape model) over the smoothing horizon. Our key contribution is to show that despite the non-convexity of the fixed-lag smoothing problem, we can solve it to certifiable optimality using a small-size semidefinite relaxation. We also present a fast outlier rejection scheme that filters out incorrect keypoint detections with shape and time compatibility tests, and wrap our certifiable solver in a graduated non-convexity scheme. We evaluate the proposed approach on synthetic and real data, showcasing its performance in a table-top manipulation scenario and a drone-based vehicle tracking application.

Mixed Diffusion for 3D Indoor Scene Synthesis

Realistic conditional 3D scene synthesis significantly enhances and accelerates the creation of virtual environments, which can also provide extensive training data for computer vision and robotics research among other applications. Diffusion models have shown great performance in related applications, e.g., making precise arrangements of unordered sets. However, these models have not been fully explored in floor-conditioned scene synthesis problems. We present MiDiffusion, a novel mixed discrete-continuous diffusion model architecture, designed to synthesize plausible 3D indoor scenes from given room types, floor plans, and potentially pre-existing objects. We represent a scene layout by a 2D floor plan and a set of objects, each defined by its category, location, size, and orientation. Our approach uniquely implements structured corruption across the mixed discrete semantic and continuous geometric domains, resulting in a better conditioned problem for the reverse denoising step. We evaluate our approach on the 3D-FRONT dataset. Our experimental results demonstrate that MiDiffusion substantially outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis. In addition, our models can handle partial object constraints via a corruption-and-masking strategy without task specific training. We show MiDiffusion maintains clear advantages over existing approaches in scene completion and furniture arrangement experiments.

Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs

We present a novel approach for long-term human trajectory prediction, which is essential for long-horizon robot planning in human-populated environments. State-of-the-art human trajectory prediction methods are limited by their focus on collision avoidance and short-term planning, and their inability to model complex interactions of humans with the environment. In contrast, our approach overcomes these limitations by predicting sequences of human interactions with the environment and using this information to guide trajectory predictions over a horizon of up to 60s. We leverage Large Language Models (LLMs) to predict interactions with the environment by conditioning the LLM prediction on rich contextual information about the scene. This information is given as a 3D Dynamic Scene Graph that encodes the geometry, semantics, and traversability of the environment into a hierarchical representation. We then ground these interaction sequences into multi-modal spatio-temporal distributions over human positions using a probabilistic approach based on continuous-time Markov Chains. To evaluate our approach, we introduce a new semi-synthetic dataset of long-term human trajectories in complex indoor environments, which also includes annotations of human-object interactions. We show in thorough experimental evaluations that our approach achieves a 54% lower average negative log-likelihood (NLL) and a 26.5% lower Best-of-20 displacement error compared to the best non-privileged baselines for a time horizon of 60s.

Clio: Real-time Task-Driven Open-Set 3D Scene Graphs

Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. While traditional closed-set metric-semantic maps were restricted to tens or hundreds of semantic classes, we can now build maps with a plethora of objects and countless semantic variations. This leaves us with a fundamental question: what is the right granularity for the objects (and, more generally, for the semantic concepts) the robot has to include in its map representation? While related work implicitly chooses a level of granularity by tuning thresholds for object detection, we argue that such a choice is intrinsically task-dependent. The first contribution of this paper is to propose a task-driven 3D scene understanding problem, where the robot is given a list of tasks in natural language and has to select the granularity and the subset of objects and scene structure to retain in its map that is sufficient to complete the tasks. We show that this problem can be naturally formulated using the Information Bottleneck (IB), an established information-theoretic framework. The second contribution is an algorithm for task-driven 3D scene understanding based on an Agglomerative IB approach, that is able to cluster 3D primitives in the environment into task-relevant objects and regions and executes incrementally. The third contribution is to integrate our task-driven clustering algorithm into a real-time pipeline, named Clio, that constructs a hierarchical 3D scene graph of the environment online using only onboard compute, as the robot explores it. Our final contribution is an extensive experimental campaign showing that Clio not only allows real-time construction of compact open-set 3D scene graphs, but also improves the accuracy of task execution by limiting the map to relevant semantic concepts.

Task and Motion Planning in Hierarchical 3D Scene Graphs

Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale hybrid metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however how to derive a planning domain from a 3D scene graph that enables efficient computation of executable plans is an open question. In this work, we present a novel approach for defining and solving Task and Motion Planning problems in large-scale environments using hierarchical 3D scene graphs. We identify a method for building sparse problem domains which enable scaling to large scenes, and propose a technique for incrementally adding objects to that domain during planning time to avoid wasting computation on irrelevant elements of the scene graph. We test our approach in two hand crafted domains as well as two scene graphs built from perception, including one constructed from the KITTI dataset. A video supplement is available at https://youtu.be/63xuCCaN0I4.

GMKF: Generalized Moment Kalman Filter for Polynomial Systems with Arbitrary Noise

This paper develops a new filtering approach for state estimation in polynomial systems corrupted by arbitrary noise, which commonly arise in robotics. We first consider a batch setup where we perform state estimation using all data collected from the initial to the current time. We formulate the batch state estimation problem as a Polynomial Optimization Problem (POP) and relax the assumption of Gaussian noise by specifying a finite number of moments of the noise. We solve the resulting POP using a moment relaxation and prove that under suitable conditions on the rank of the relaxation, (i) we can extract a provably optimal estimate from the moment relaxation, and (ii) we can obtain a belief representation from the dual (sum-of-squares) relaxation. We then turn our attention to the filtering setup and apply similar insights to develop a GMKF for recursive state estimation in polynomial systems with arbitrary noise. The GMKF formulates the prediction and update steps as POPs and solves them using moment relaxations, carrying over a possibly non-Gaussian belief. In the linear-Gaussian case, GMKF reduces to the standard Kalman Filter. We demonstrate that GMKF performs well under highly non-Gaussian noise and outperforms common alternatives, including the Extended and Unscented Kalman Filter, and their variants on matrix Lie group.

Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments

Perceiving and understanding highly dynamic and changing environments is a crucial capability for robot autonomy. While large strides have been made towards developing dynamic SLAM approaches that estimate the robot pose accurately, a lesser emphasis has been put on the construction of dense spatio-temporal representations of the robot environment. A detailed understanding of the scene and its evolution through time is crucial for long-term robot autonomy and essential to tasks that require long-term reasoning, such as operating effectively in environments shared with humans and other agents and thus are subject to short and long-term dynamics. To address this challenge, this work defines the Spatio-temporal Metric-semantic SLAM (SMS) problem, and presents a framework to factorize and solve it efficiently. We show that the proposed factorization suggests a natural organization of a spatio-temporal perception system, where a fast process tracks short-term dynamics in an active temporal window, while a slower process reasons over long-term changes in the environment using a factor graph formulation. We provide an efficient implementation of the proposed spatio-temporal perception approach, that we call Khronos, and show that it unifies exiting interpretations of short-term and long-term dynamics and is able to construct a dense spatio-temporal map in real-time. We provide simulated and real results, showing that the spatio-temporal maps built by Khronos are an accurate reflection of a 3D scene over time and that Khronos outperforms baselines across multiple metrics. We further validate our approach on two heterogeneous robots in challenging, large-scale real-world environments.