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• The paper proposes a novel 3-D relative localization framework using inter-robot interior angle and self-displacement measurements.method 95%
• Robots can determine their neighbors' relative positions and orientations in a purely linear manner using the proposed linear relative localization algorithm.result 90%
• The proposed MAP estimator addresses three primary challenges: non-convexity leading to local optima, lack of prior probability density knowledge, and increasing computational cost.technical 90%
• The linear computation process is reformulated as a Weighted Total Least Squares (WTLS) optimization problem on manifolds to reduce the risk of local optima.method 85%
• The WTLS optimal solution provides more accurate initial values for solving the MAP optimization problem.technical 80%
• A Neural Density Estimator (NDE) is combined with WTLS to address the lack of prior probability density knowledge.method 85%
• A marginalization mechanism ensures constant computational cost despite continuous robot movement.technical 85%
• The proposed framework is specifically designed to handle measurement noise in relative localization.technical 80%

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