LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods.

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at https://github.com/PengYu-team/Co-LRIO.

Evaluation is the foundation of automated program repair (APR), as it provides empirical evidence on strengths and weaknesses of APR techniques. However, the reliability of such evaluation is often threatened by various introduced biases. Consequently, bias exploration, which uncovers biases in the APR evaluation, has become a pivotal activity and performed since the early years when pioneer APR techniques were proposed. Unfortunately, there is still no methodology to support a systematic comprehension and discovery of evaluation biases in APR, which impedes the mitigation of such biases and threatens the evaluation of APR techniques. In this work, we propose to systematically understand existing evaluation biases by rigorously conducting the first systematic literature review on existing known biases and systematically uncover new biases by building a taxonomy that categorizes evaluation biases. As a result, we identify 17 investigated biases and uncover a new bias in the usage of patch validation strategies. To validate this new bias, we devise and implement an executable framework APRConfig , based on which we evaluate three typical patch validation strategies with four representative heuristic-based and constraint-based APR techniques on three bug datasets. Overall, this article distills 13 findings for bias understanding, discovery, and validation. The systematic exploration we performed and the open source executable framework we proposed in this article provide new insights as well as an infrastructure for future exploration and mitigation of biases in APR evaluation.

In this paper, a low-cost vision-based autonomous landing architecture for an unmanned aerial vehicle (UAV) on a moving platform is presented. First, a novel landing pad was designed for a monocular camera to robustly detect the pad in both high and low altitudes. In order to solve mirror effect and occasional misidentification, a 3D points cluster algorithm for relative position estimation is presented. Second, the dynamics of the quadrotor is simplified for this lading task and a PD controller is designed based on the estimated relative position. Finally, the low-cost system architecture of the quadrotor and the experiment results are both presented to show the effectiveness of the proposed method.

Spectrum-based fault localization (SFL), the technique producing a rank list of statements in descending order of their suspiciousness values, is nowadays widely used in current automated program repair tools. There are two different algorithms for these tools to choose statements selected for modification to produce candidate patches from the list: one is the rank-first algorithm based on suspiciousness rankings of statements, the other is the suspiciousness-first algorithm based on suspiciousness value of statements. However, to our knowledge there is no research work implementing the two algorithms in the same repair tool or comparing their effectiveness. In this paper, we conduct an empirical research based on the automated repair tool Nopol with the benchmark set of Defects4J to compare these two algorithms. Preliminary results suggest that the suspiciousness-first algorithm is not equivalent to the rank-first algorithm and behaves better in parallel repair and patch diversity.

Automated program repair recently received considerable attentions, and many techniques on this research area have been proposed. Among them, two genetic-programming-based techniques, GenProg and Par, have shown the promising results. In particular, GenProg has been used as the baseline technique to check the repair effectiveness of new techniques in much literature. Although GenProg and Par have shown their strong ability of fixing real-life bugs in nontrivial programs, to what extent GenProg and Par can benefit from genetic programming, used by them to guide the patch search process, is still unknown.

Researchers have proposed many spectra-based fault localization (SBFL) techniques in the past decades. Existing studies evaluate the effectiveness of these techniques from the viewpoint of developers, and have drawn some important conclusions through either empirical study or theoretical analysis. In this paper, we present the first study on the effectiveness of SBFL techniques from the viewpoint of fully automated debugging including the program repair of automation, for which the activity of automated fault localization is necessary. We assess the accuracy of fault localization according to the repair effectiveness in the automated repair process guided by the localization technique. Our experiment on 14 popular SBFL techniques with 11 subject programs shipping with real-life field failures presents the evidence that some conclusions drawn in prior studies do not hold in our experiment. Based on experimental results, we suggest that Jaccard should be used with high priority before some more effective SBFL techniques specially proposed for automated program repair occur in the future.

Most techniques for automated program repair use test cases to validate the effectiveness of the produced patches. The validation process can be time-consuming especially when the object programs ship with either lots of test cases or some long-running test cases. To alleviate the cost for testing, we first introduce regression test prioritization insight into the area of automated program repair, and present a novel prioritization technique called FRTP with the goal of reducing the number of test case executions in the repair process. Unlike most existing prioritization techniques frequently requiring additional cost for gathering previous test executions information, FRTP iteratively extracts that information just from the repair process, and thus incurs trivial performance lose. We also built a tool called TrpAutoRepair, which implements our FRTP technique and has the ability of automatically repairing C programs. To evaluate TrpAutoRepair, we compared it with GenProg, a state-of-the-art tool for automated C program repair. The experiment on the 5 subject programs with 16 real-life bugs provides evidence that TrpAutoRepair performs at least as good as GenProg in term of success rate, in most cases (15/16), TrpAutoRepair can significantly improve the repair efficiency by reducing efficiently the test case executions when searching a valid patch in the repair process.

Many techniques on automated fault localization (AFL) have been introduced to assist developers in debugging. Prior studies evaluate the localization technique from the viewpoint of developers: measuring how many benefits that developers can obtain from the localization technique used when debugging. However, these evaluation approaches are not always suitable, because it is difficult to quantify precisely the benefits due to the complex debugging behaviors of developers. In addition, recent user studies have presented that developers working with AFL do not correct the defects more efficiently than ones working with only traditional debugging techniques such as breakpoints, even when the effectiveness of AFL is artificially improved. In this paper we attempt to propose a new research direction of developing AFL techniques from the viewpoint of fully automated debugging including the program repair of automation, for which the activity of AFL is necessary. We also introduce the NCP score as the evaluation measurement to assess and compare various techniques from this perspective. Our experiment on 15 popular AFL techniques with 11 subject programs shipping with real-life field failures presents the evidence that these AFL techniques performing well in prior studies do not have better localization effectiveness according to NCP score. We also observe that Jaccard has the better performance over other techniques in our experiment.

For large-scale programs, automatically repairing a bug by modifying source code is often a time-consuming process due to plenty of time spent on recompiling and reinstalling the patched program. To suppress the above time cost and make the repair process more efficient, a recompilation technique called weak recompilation is described in this paper. In weak recompilation, a program is assumed to be constructed from a set of components, and for each candidate patch only the changed code fragment in term of one component is recompiled to a shared library; the behaviors of patched program are observed by executing the original program with an instrumentation tool which can wrap specified function. The advantage of weak recompilation is that redundant recompilation cost can be suppressed, and reinstallation cost will be cut down completely. We also built WAutoRepair, a system which enables scalability to fix bugs in large-scale C programs with high efficiency. The experiments confirm that our repair system significantly outperforms Genprog, a famous approach for automatic program repair. For the wireshark program containing over 2 millions lines of code, WAutoRepair spent only 0.222 seconds in recompiling one candidate patch and 8.035 seconds in totally repairing the bug, compared to Genprog separately taking about 20.484 and 75.493 seconds, on average.